Systems and methods for the determination of insulin sensitivity

ABSTRACT

A subject is prescribed short and long acting insulin medicament regimens. When a qualified fasting event occurs, the basal insulin sensitivity estimate of the subject is updated using (i) an expected fasting blood glucose level based upon the long acting insulin medicament dosing specified by the long acting regimen during the fasting event, (ii) glucose measurements contemporaneous with the fasting event and (iii) a prior insulin sensitivity factor. A basal insulin sensitivity factor curve is calculated from the updated basal insulin sensitivity estimate. A bolus insulin sensitivity estimate of the subject is updated upon occurrence of a correction bolus with a short acting insulin medicament using (i) an expected blood glucose level based upon the correction bolus, (ii) glucose measurements after occurrence of the correction bolus, and (iii) a prior insulin sensitivity factor. A bolus insulin sensitivity factor curve is calculated from the updated bolus insulin sensitivity estimate.

TECHNICAL FIELD

The present disclosure relates generally to systems, methods andcomputer programs for assisting patients and health care practitionersin estimating insulin sensitivity and using such information forpurposes such as providing a recommended dose of a short acting insulinmedicament to achieve a target fasting glucose level in a subject.

BACKGROUND

Type 2 diabetes mellitus is characterized by progressive disruption ofnormal physiologic insulin secretion. In healthy individuals, basalinsulin secretion by pancreatic β cells occurs continuously to maintainsteady glucose levels for extended periods between meals. Also inhealthy individuals, there is prandial secretion in which insulin israpidly released in an initial first-phase spike in response to a meal,followed by prolonged insulin secretion that returns to basal levelsafter 2-3 hours.

Insulin is a hormone that binds to insulin receptors to lower bloodglucose by facilitating cellular uptake of glucose, amino acids, andfatty acids into skeletal muscle and fat and by inhibiting the output ofglucose from the liver. In normal healthy individuals, physiologic basaland prandial insulin secretions maintain euglycemia, which affectsfasting plasma glucose and postprandial plasma glucose concentrations.Basal and prandial insulin secretion is impaired in Type 2 diabetes andearly post-meal response is absent. To address these adverse events,patients with Type 2 diabetes are provided with insulin treatmentregimens. Patients with Type 1 diabetes are also provided with insulintreatment regimens.

Some diabetic patients only need a basal insulin treatment regimen tomake up for deficiencies in pancreatic β cells insulin secretion. Otherdiabetic patients need both basal and bolus insulin treatment.

Patients that require both basal and bolus insulin treatment take aperiodic basal insulin medicament treatment, for instance once or twicea day, as well as one or more bolus insulin medicament treatments withmeals. Such multiple daily injection (MDI) insulin therapy usuallyinvolves injections of fast acting insulin before each meal andlong-acting insulin once to twice per day. Most patients undergoinginsulin therapy for managing their diabetes have difficulty determininghow much insulin they need. The size of a dose depends on how manycarbohydrates the patient consumed over a particular period, how fartheir current blood glucose levels are from a target level, as well ascurrent physiological state such as insulin sensitivity. Traditionally,two parameters are used to calculate fast-acting insulin doses (i)insulin sensitivity factor (ISF), which is used to calculate how muchinsulin is needed to move glucose levels to a desired target, and (ii)carb-to-insulin ratio (CIR), which is used to calculate how much insulinis needed to account for a meal. Since these factors are used tocalculate the amount of insulin medicament to dose, it is important thatthey are correct and relate to the current physiological state of thepatient. Otherwise, the dosing will be imprecise and result insuboptimal treatment and episodes of hyper- and hypoglycaemic events.

Patients' physiological state affects their insulin sensitivity andthereby how much insulin they need to account for meals and too highglucose levels. Situations where insulin sensitivity changes includeperiods of illness (e.g. fever, influenza etc.) at which time insulinsensitivity typically decreases. They also include periods of highlevels of physical activity, at which time insulin sensitivity typicallyincreases. They further include periods of high levels of stress, whichcan cause insulin sensitivity to decrease, but typically recovers afterthe stress has passed. Patients experience a high level of difficultyduring these periods in controlling their blood glucose levels. Theirpredefined parameters do not apply for dose calculations and theyexperience hyper- and hypoglycaemic events and a feeling of loss ofcontrol.

Determining patient ISF and CIR values is a challenge to health careprofessionals and patients and it requires extensive work. For a healthcare professional to accurately estimate the ISF, the HCP needs accessto extensive and reliable data including glucose measurement data,insulin doses, as well as other information that may affect thepatients' physiological state. This data needs to be provided by thepatient, which is time consuming and inconvenient. Many health careprofessionals have difficulty determining ISF from all of the data andtend to guess at the value based upon only one or two data points.Furthermore, the patient may not have sufficiently frequent access to ahealth care professional to make adequate adjustments to the patient'sdiabetes management formula and/or its various factors. Furthermore,health care professionals and patients cannot calculate insulinsensitivity frequently enough to capture changes during periods ofstress, illness etc.

Conventional automated dose calculators typically update the ISFestimate based on glucose response to fast acting insulin, eitherfollowing a meal-related dose or a correction bolus. This posesproblems. For instance, if the ISF is estimated based on meal-relateddoses, high uncertainty may be expected due to the high uncertainty inCHO-counting. If the ISF is estimated based on a correction bolus only,this requires that a correction bolus is taken frequently. If this isnot done, for instance because the patient is in relatively goodglycaemic control, the ISF is not updated frequently and periods ofincreased insulin sensitivity may not be detected.

WO 2012/122520 discloses that patient's using long-acting insulin mayhave different sensitivity to insulin. It further discloses thatmeasurements of a fasting blood glucose level can be measured andcompared to a predetermined threshold value, that a dosagerecommendation algorithm can be used to estimate a dose pased on thefasting blood glucose level and the threshold value. The document alsodiscloses that a measurement of the blood glucose level can be used totitrate the long-acting insulin dose until a threshold range isachieved. WO 2012/122520 further shows that a patient's insulinsensitivity can be based on the patient's fasting blood glucose leveland the fasting insulin level. However, WO 2012/122520 does not solvethe problem of ensuring that parameters for dose estimation isfrequently updated, in particular if no correction bolus is taken.

Given the above background, what is needed in the art are systems andmethods that provide satisfactory ways to estimate parameters in aninsulin medicament regimen, such as ISF, CSF, and related parameters.

SUMMARY

The present disclosure addresses the above-identified need in the art byproviding methods, devices, computer programs and computer readablecarriers having stored thereon computer programs for estimatingparameters in an insulin medicament regimen. In particular, embodimentsof the present disclosure relate generally to a method and apparatus forassisting patients and health care practitioners in managing insulintreatment to diabetic patients. In one aspect, insulin sensitivity isestimated based on data sets of continuous glucose monitoring data andinsulin pen data where at least two types of insulin medicament areused, for instance, a fast acting insulin medicament and a long actinginsulin medicament.

In a first aspect of the present disclosure is proved a device forestimating parameters in an insulin medicament regimen for a subjectthat includes both a short acting insulin medicament regimen and a longacting insulin medicament regimen, and wherein the device comprises oneor more processors and a memory, the memory storing instructions that,when executed by the one or more processors, perform a method of:

-   -   A) obtaining:        -   A.1) a first data set, the first data set comprising a            plurality of glucose measurements of the subject taken over            a first period of time and, for each respective glucose            measurement in the plurality of glucose measurements, a            timestamp representing when the respective measurement was            made;        -   A.2) a second data set from one or more insulin pens used by            the subject to apply the insulin medicament dosage regimen,            the second data set comprises a plurality of insulin            medicament records, each insulin medicament record in the            plurality of medicament records comprises: (i) a respective            insulin medicament injection event including an amount of            insulin medicament injected into the subject using a            respective insulin pen in the one or more insulin pens (ii)            a corresponding electronic timestamp that is automatically            generated by the respective insulin pen upon occurrence of            the respective insulin medicament injection event, and (iii)            a type of insulin medicament, wherein the type of insulin            medicament is a short acting insulin medicament or a long            acting insulin medicament;    -   B) determining one or more insulin sensitivity change estimates        by:        -   B.1) making an estimate of a basal insulin sensitivity            change between a basal insulin sensitivity estimate            (ISF_(basal,i,t)) for the subject related to the occurrence            of a first basal insulin related event undertaken by the            subject within the first period of time, when the first            basal insulin related event is deemed qualified, and a basal            insulin sensitivity estimate (ISF_(basal,i-p,t)) of the            subject related to the occurrence of a qualified basal            insulin related event occurring before the first basal            insulin related event, wherein the occurrence of the basal            insulin related events are identified in the first data set,            the estimating the change using:            -   (i) the basal insulin sensitivity estimate                (ISF_(basal,i-p,t)) of the subject related to the                occurrence of the qualified basal insulin related event                occurring before the first basal insulin related                event (ii) glucose measurements from the first data set                contemporaneous with the occurrence of the first                qualified basal insulin related event, (iii) glucose                measurements from the first data set contemporaneous                with the occurrence of the qualified basal insulin                related event occurring before the first qualified basal                insulin related event, (iv) an insulin medicament                injection event from the second data set corresponding                to the first qualified basal insulin related event,                wherein the injection has been applied according to the                long acting insulin medicament regimen, and (v) an                insulin medicament injection event from the second data                set corresponding to the qualified basal insulin related                event occurring before the first qualified basal insulin                related event, wherein the injection has been applied                according to the long acting insulin medicament regimen;                and/or        -   B.2) making an estimate of a bolus insulin sensitivity            change between a bolus insulin sensitivity estimate            (ISF_(bolus,i,t)) for the subject relating to the occurrence            of a correction bolus with a short acting insulin medicament            within the first period of time and a bolus insulin            sensitivity estimate (ISF_(bolus,i-p,t)) of the subject            related to the occurrence of a prior correction bolus with            the short acting insulin medicament, wherein the occurrence            of the correction bolus and the occurrence of the prior            correction bolus are identified in the first data set, the            estimating the change using: (i) glucose measurements from            the first data set contemporaneous with the occurrence of            the correction bolus with a short acting medicament, (ii)            the bolus insulin sensitivity estimate (ISF_(bolus,i-p,t))            of the subject related to the occurrence of a prior            correction bolus with the short acting insulin medicament            and (iii) an insulin medicament injection event from the            second data set corresponding to the occurrence of the            correction bolus and applied according to the short acting            insulin medicament regimen (214); and    -   C) estimating the bolus insulin sensitivity estimate        (ISF_(bolus,i,t)) as a function of the estimated basal insulin        sensitivity change, in response to making an estimate of the        basal insulin sensitivity change in B.1); and/or    -   D) estimating the basal insulin sensitivity estimate        (ISF_(basal,i,t)) as a function of the estimated bolus insulin        sensitivity change, in response to making an estimate of the        bolus insulin sensitivity change in B.2), and        -   wherein the insulin sensitivity estimates are parameters in            the insulin medicament regimen.

Hereby is provided a more robust ISF estimator based on bolus and basalinsulin injections, where a new bolus or basal ISF estimate is based onan estimate of basal or bolus insulin sensitivity change, respectively.In the present disclosure, the insulin sensitivity factor (ISF) can beestimated based on a drop in glucose level following a correction bolusand/or a change in fasting glucose levels response following a basalinsulin injection. The identification of a change between the basalinsulin sensitivity estimate (ISF_(basal,i,t)) of the subject during afirst basal insulin related event and the basal insulin sensitivityestimate (ISF_(basal,i-p,t)) of the subject during a qualified basalinsulin related event occurring before the first basal insulin relatedevent in B.1), enables the estimating of a change in an insulinsensitivity of the subject, wherein the estimated change in insulinsensitivity can be used to calculate a new bolus insulin sensitivityestimate. Similarly, the identification of a change between the bolusinsulin sensitivity estimate (ISF_(bolus,i,t)) for the subject relatingto the occurrence of a correction bolus with a short acting insulinmedicament within the first period of time and a bolus insulinsensitivity estimate (ISF_(bolus,i-p,t)) of the subject related to theoccurrence of a prior correction bolus with the short acting insulinmedicament, can be used to calculate a new basal insulin sensitivityestimate. As the bolus insulin sensitivity estimate can be based on anevent related to a basal insulin related event, and as the basal insulinsensitivity estimate can be based on an event related to a correctionbolus, i.e., a bolus insulin related event, the ISF estimator is morerobust, and more frequent parameter updates are enabled. The first andsecond data set systematically provides timestamped data, and therebycontributes to the reliability of the estimated parameters, and therobustness of the estimator. If for example a change in insulinsensitivity is not detected by the ISF estimator, the next ISFcalculation will be less correct, whereas an efficient detection of achange in ISF will increase the validity. Therefore, the density ofglucose influences the ability to identify insulin related events, andthe injection data provided directly by one or more insulin pensincreases the quality of the estimated parameters, as well as it enablesadherence categorization based on stamped time-insulin injections.

In a further aspect, the estimated basal insulin sensitivity change is afunction of the estimated basal insulin sensitivity estimate(ISF_(basal,i,t)) for the subject upon occurrence of the first basalinsulin related event and the basal insulin sensitivity factor(ISF_(basal,i-p,t)) of the subject during the qualified basal insulinrelated event occurring before the first bolus insulin relevant event.

In a further aspect, the estimated bolus insulin sensitivity change is afunction of the estimated bolus insulin sensitivity estimate(ISF_(bolus,i,t)) for the subject upon occurrence of the correctionbolus with a short acting insulin medicament and the bolus insulinsensitivity factor (ISF_(bolus,i-p,t)) of the subject estimated basedupon occurrence of a prior correction bolus with the short actinginsulin medicament.

In a further aspect, the method further comprising:

-   -   E) estimating a basal insulin sensitivity factor curve        (ISF_(basal,i)) as a function of the estimated basal insulin        change, in response to estimating the basal insulin sensitivity        change in B.1) and/or in response to estimating the basal        insulin sensitivity estimate in D).

In a further aspect, the estimating the basal insulin sensitivity factorcurve (ISF_(basal,i)) in E) comprises computing:

${{ISF}_{{basal}.i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

wherein ISF_(basal,i-p) represents a prior basal sensitivity factorcurve estimate.

In a further aspect, the method further comprising:

-   -   F) estimating a bolus insulin sensitivity factor curve        (ISF_(bolus,i)) as a function of (i) the estimated bolus insulin        sensitivity change, in response to estimating the bolus insulin        sensitivity change in B2) and/or in response to estimating the        bolus insulin sensitivity in C).

In a further aspect, the estimating the bolus sensitivity factor curve(ISF_(bolus,i)) in F) comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

wherein ISF_(bolus,i-p) represents a prior bolus sensitivity factorcurve estimate.

In a further aspect, the method further comprising:

-   -   G) updating        -   (i) the bolus insulin sensitivity curve (ISF_(bolus)) as a            function of the estimated bolus insulin sensitivity factor            curve (ISF_(bolus,i)) of F) and prior estimated bolus            insulin sensitivity factor curves for the subject, and        -   (ii) updating the basal insulin sensitivity curve            (ISF_(basal)) as a function of the estimated basal insulin            sensitivity factor curve (ISF_(basal,i)) of E) and prior            estimated basal insulin sensitivity factor curves for the            subject; and    -   H) providing a recommended dose of the short acting insulin        medicament to achieve a target fasting glucose level in the        subject by using glucose measurements from a portion of the        plurality of glucose measurements and the updated bolus insulin        sensitivity curve (ISF_(bolus)) or the updated basal insulin        sensitivity curve (ISF_(basal)).

In a further aspect, the estimating the basal insulin sensitivity changefor the subject in B.1) is computed as:

( ISF basal , i ISF basal , i - p ) = ( FBG expected - i i + 1 ) ,

wherein FBG_(expected) the expected blood glucose level (FBG_(expected))in a period of time during the first qualified basal insulin relatedevent based on (i) the basal insulin sensitivity estimate(ISF_(basal,i-p,t)) of the subject related to the occurrence of thequalified basal insulin related event occurring before the first basalinsulin related event (ii) the glucose measurements from the first dataset contemporaneous with the occurrence of the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent, (iii) the insulin medicament injection event from the second dataset corresponding to the first qualified basal insulin related event,and (iv) the insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, and wherein

_(i) is the glucose level (

_(i)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the first qualified basal insulinrelated event.

In a further aspect, the estimating the basal insulin sensitivity changefor the subject in B.1) is computed as:

( ISF basal , i , t ISF basal , i - p , t ) = ( i - i - p FBG expected -i - p ) ,

wherein

_(i) is the glucose level (

_(i)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the first qualified basal insulinrelated event, wherein FBG_(expected) is the expected blood glucoselevel (FBG_(expected)) during the first basal insulin related eventbased on (i) the basal insulin sensitivity estimate (ISF_(basal,i-p,t))of the subject related to the occurrence of the qualified basal insulinrelated event occurring before the first basal insulin related event(ii) the glucose measurements from the first data set contemporaneouswith the occurrence of the qualified basal insulin related eventoccurring before the first qualified basal insulin related event, (iii)the insulin medicament injection event from the second data setcorresponding to the first qualified basal insulin related event, and(iv) the insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, and wherein

_(i-p) is the glucose level (

_(i-p)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent, and wherein FBG_(expected) is different from

_(i-p).

In a further aspect, the expected blood glucose level (FBG_(expected))is computed as:

FBG expected = basal , i - p - ISF basal , i - p , t  ( U basal , i - Ubasal , i - p ) ,

wherein

_(basal,i-p) is the glucose level (

_(basal,i-p)) based on the glucose measurements from the first data setcontemporaneous with the occurrence of the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent, wherein U_(basal, i) is the amount of insulin medicament(U_(basal,i)) corresponding to the insulin medicament injection eventfrom the second data set corresponding to the first qualified basalinsulin related event, U_(basal, i-1) is the amount of insulin(U_(basal,i-p)) corresponding to the insulin medicament injection eventfrom the second data set corresponding to the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent.

In a further aspect, the first basal insulin related event is deemedqualified when (i) the subject has taken no correction bolus of theshort acting insulin medicament in the twelve hours prior to the firstbasal insulin related event and (ii) the subject has taken a meal bolusof the short acting insulin medicament with each hypoglycaemic eventfree meal in the fourteen hours prior to the first fasting event,wherein the occurrence of a correction bolus, a first basal insulinrelated event, a hypoglycaemic event free meal are identified in thefirst data set

In a further aspect, the occurrence of a correction bolus is furtheridentified in the second data set.

In a further aspect, the estimating the bolus insulin sensitivity changein B.2) is computed as:

( ISF bolus , i , t ISF bolus , i - p , t ) = ( BG expected - bolus ,corr , i bolus , corr , i + 1 ) ,

wherein BG_(expected) is the expected blood glucose level(BG_(expected)) based on (i) the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, (ii) the bolus insulin sensitivityestimate (ISF_(bolus,i-p,t)) of the subject related to the occurrence ofthe prior correction bolus with the short acting insulin medicament and(iii) an insulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus, and wherein

_(bolus,corr,i) is the glucose level (

_(bolus,corr,i)) of the subject after the occurrence of the correctionbolus, wherein

_(bolus,corr,i) is obtained from the portion of the glucose measurementsof the first data set that are contemporaneous with a period of timeafter the occurrence of the correction bolus, and whereby the portion ofthe glucose measurements is a subset of the measurements that arecontemporaneous with the occurrence of the correction bolus with a shortacting medicament.

In a further aspect, the estimating the bolus insulin sensitivity changein B.2) is computed as:

( ISF bolus , i , t ISF bolus , i - p , t ) = ( bolus , hyp , i - bolus, corr , i bolus , corr , i - BG expected ) ,

wherein

_(bolus,hyp,i) is the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, wherein

_(bolus,corr,i) is the glucose level (

_(corr,i)) of the subject after occurrence of the correction bolus,wherein

_(bolus,corr,i) is the glucose level (

_(bolus,corr,i)) of the subject after the occurrence of the correctionbolus, wherein

_(bolus,corr,i) is obtained from the portion of the glucose measurementsof the first data set that are contemporaneous with a period of timeafter the occurrence of the correction bolus, and whereby the portion ofthe glucose measurements is a subset of the measurements that arecontemporaneous with the occurrence of the correction bolus with a shortacting medicament, and wherein BG_(expected) is the expected bloodglucose level (BG_(expected)) based on (i) the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, (ii) the bolus insulin sensitivityestimate (ISF_(bolus,i-p,t)) of the subject related to the occurrence ofthe prior correction bolus with the short acting insulin medicament and(iii) an insulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus.

In a further aspect, the expected blood glucose level (BG_(expected)) iscomputed as:

BG _(expected)=

_(bolusl,hyp,i) −ISF _(bolus,i-p,t) U _(bolus,i).

In a further aspect, the estimating the bolus insulin sensitivity curve(ISF_(bolus,i)) as a function of the estimated basal insulin sensitivitychange, in response to estimating the basal insulin sensitivity changein B.1) comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

wherein ISF_(bolus,i-p) represents a prior bolus sensitivity factorcurve estimate.

In a further aspect, the estimating the basal insulin sensitivity curve(ISF_(basal,i)) as a function of the estimated bolus insulin sensitivitychange, in response to estimating the bolus insulin sensitivity changein B2) comprises computing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

wherein ISF_(basal,i-p) represents a prior basal sensitivity factorcurve estimate.

In a further aspect, the updating the bolus insulin sensitivity factorcurve comprises computing:

${{ISF}_{bolus} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{bolus},n}}}},$

wherein,

-   -   q is a predetermined number of historical updates to the bolus        insulin sensitivity curve (ISF_(bolus)),    -   w is a linear or nonlinear vector of normalised weights,    -   n is an integer index into the historical updates to the bolus        insulin sensitivity curve (ISF_(bolus)) and vector w, and    -   ISF_(bolus,n) is an n^(th) past bolus insulin sensitivity curve        (ISF_(bolus)) curve.

In a further aspect, the updating the basal insulin sensitivity factorcurve comprises computing:

${ISF}_{basal} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{basal},n}}}$

wherein,

-   -   q is a predetermined number of historical updates to the basal        insulin sensitivity curve (ISF_(basal)),    -   w is a linear or nonlinear vector of normalised weights,    -   n is an integer index into the historical updates to the basal        insulin sensitivity curve (ISF_(basal)) and vector w, and    -   ISF_(basal,n) is an n^(th) past basal insulin sensitivity curve        (ISF_(basal)).

In a further aspect, the method further comprises:

-   -   obtaining a third data set (238), the third data set comprising        a plurality of physiological measurements of the subject taken        over the first period of time and, for each respective        physiological measurement (240) in the plurality of        physiological measurements, a physiological measurement        timestamp (242) representing when the respective physiological        measurement was made; and wherein    -   a value of p is determined by the plurality of physiological        measurements.

In a further aspect, each physiological measurement is a measurement ofbody temperature of the subject and wherein p is reduced during periodswhen the subject has an elevated temperature.

In a further aspect, each physiological measurement is a measurement ofactivity of the subject and wherein p is reduced during periods when thesubject is incurring elevated activity.

In a further aspect is provided a method for estimating parameters in aninsulin medicament dosage regimen for a subject that includes both ashort acting insulin medicament regimen and a long acting insulinmedicament regimen, the method comprising:

-   -   A) obtaining:        -   A.1) a first data set, the first data set comprising a            plurality of glucose measurements of the subject taken over            a first period of time and, for each respective glucose            measurement in the plurality of glucose measurements, a            timestamp representing when the respective measurement was            made;        -   A.2) a second data set from one or more insulin pens used by            the subject to apply the insulin medicament dosage regimen,            the second data set comprises a plurality of insulin            medicament records, each insulin medicament record in the            plurality of medicament records comprises: (i) a respective            insulin medicament injection event including an amount of            insulin medicament injected into the subject using a            respective insulin pen in the one or more insulin pens (ii)            a corresponding electronic timestamp that is automatically            generated by the respective insulin pen upon occurrence of            the respective insulin medicament injection event, and (iii)            a type of insulin medicament, wherein the type of insulin            medicament is a short acting insulin medicament or a long            acting insulin medicament;    -   B) determining one or more insulin sensitivity change estimates        by:        -   B.1) making an estimate of a basal insulin sensitivity            change between a basal insulin sensitivity estimate            (ISF_(basal,i,t)) for the subject related to the occurrence            of a first basal insulin related event undertaken by the            subject within the first period of time, when the first            basal insulin related event is deemed qualified, and a basal            insulin sensitivity estimate (ISF_(basal,i-p,t)) of the            subject related to the occurrence of a qualified basal            insulin related event occurring before the first basal            insulin related event, wherein the occurrence of the basal            insulin related events are identified in the first data set,            the estimating using:            -   (i) the basal insulin sensitivity estimate                (ISF_(basal,i-p,t)) of the subject related to the                occurrence of the qualified basal insulin related event                occurring before the first basal insulin related                event (ii) glucose measurements from the first data set                contemporaneous with the occurrence of the first                qualified basal insulin related event, (iii) glucose                measurements from the first data set contemporaneous                with the occurrence of the qualified basal insulin                related event occurring before the first qualified basal                insulin related event, (iv) an insulin medicament                injection event from the second data set corresponding                to the first qualified basal insulin related event,                wherein the injection has been applied according to the                long acting insulin medicament regimen, and (v) an                insulin medicament injection event from the second data                set corresponding to the qualified basal insulin related                event occurring before the first qualified basal insulin                related event, wherein the injection has been applied                according to the long acting insulin medicament regimen;                and/or        -   B.2) making an estimate of a bolus insulin sensitivity            change between a bolus insulin sensitivity estimate            (ISF_(bolus,i,t)) for the subject relating to the occurrence            of a correction bolus with a short acting insulin medicament            within the first period of time and a bolus insulin            sensitivity estimate (ISF_(bolus,i-p,t)) of the subject            related to the occurrence of a prior correction bolus with            the short acting insulin medicament, wherein the occurrence            of the correction bolus and the occurrence of the prior            correction bolus are identified in the first data set, the            estimating using: (i) glucose measurements from the first            data set contemporaneous with the occurrence of the            correction bolus with a short acting medicament, (ii) the            bolus insulin sensitivity estimate (ISF_(bolus,i-p,t)) of            the subject related to the occurrence of a prior correction            bolus with the short acting insulin medicament and (iii) an            insulin medicament injection event from the second data set            corresponding to the occurrence of the correction bolus and            applied according to the short acting insulin medicament            regimen; and    -   C) estimating the bolus insulin sensitivity estimate        (ISF_(bolus,i,t)) as a function of the estimated basal insulin        sensitivity change, in response to making an estimate of the        basal insulin sensitivity change in B.1); and/or    -   D) estimating the basal insulin sensitivity estimate        (ISF_(basal,i,t)) as a function of the estimated bolus insulin        sensitivity change, in response to making an estimate of the        bolus insulin sensitivity change in B.2), and    -   wherein the insulin sensitivity estimates are parameters in the        insulin medicament regimen.

In a further aspect is provided, a computer program is providedcomprising instructions that, when executed by one or more processors,perform the method according to claim 23.

In a further aspect is provided, a computer-readable data carrier havingstored thereon the computer program according to claim 24.

In some embodiments, the ISF is a weighted average over a past estimatehorizon, either past few days or a specific past period known to besimilar with respect to current physiological circumstances. In someembodiments, the ISF is updated as soon as new data is available butsuspended during non-adherence. An example of non-adherence eventsinclude, for instance, following a forgotten dinner bolus, in whichglucose levels will be higher than otherwise and therefore the data isnot useful for accurate ISF estimation. If a significant change in ISFis detected, the system can either ask for user input of an explanationor use data from wearables such as temperature, blood pressure oractivity meters. If the significant change is confirmed by such adevice, the estimation horizon is shortened or moved to a known pastsimilar period. Assuming that ISF and CIR are proportionally correlated,CIR is updated proportionally according to changes in ISF, i.e. ifincrease in insulin sensitivity is observed, then less insulin istypically needed to account for CHO in a meal. By only estimating ISFbased on correction bolus and not meal-related doses, the uncertainty ofCHO counting and postprandial BG behavior is eliminated. Furthermore, byestimating ISF based on response in fasting glucose following a basalinjection, more frequent ISF estimations are available than if only donebased on bolus. Furthermore, measuring changes in ISF based on responseto two different insulins adds robustness to ISF estimation.

Adding wearable devices such as activity monitors and temperaturemeasuring devices allows additional features. For instance, atemperature measuring device (i) offers the prospect of knowing whenchanges in ISF are expected due to illness, allows for insulinmedicament titration to be set on hold during illness, alerts for timesin which detected changes in ISF should be given higher weight. Anactivity monitor offers knowing when changes in ISF are expected due toincreased activity, and alerts as to when detected changes in ISF can begiven higher weight.

Thus, major advantages of the disclosed systems and methods aretherefore a more robust estimate of insulin sensitivity which results ina more precise insulin dosage during long-term physiological changes aswell as periods of deviations such as illness, stress and increasedlevel of activity.

As an application of these techniques, a diabetic patient or a healthcare practitioner is provided with an accurate basal insulin sensitivityfactor curve and/or bolus insulin sensitivity factor curve that servesas an improved basis for providing a recommended dose of a short actinginsulin medicament to achieve a target fasting glucose level for thediabetic patient.

In one aspect of the present disclosure, systems and methods areprovided for estimating parameters in an insulin medicament regimen fora subject that includes both a short acting insulin medicament regimenand a long acting insulin medicament regimen. A first data set isobtained. The first data set comprises a plurality of glucosemeasurements of the subject taken over a period of time and, for eachrespective glucose measurement in the plurality of glucose measurements,a timestamp representing when the respective measurement was made.

A new basal insulin sensitivity estimate (ISF_(basal,i,t)) or a newbolus insulin sensitivity estimate (ISF_(bolus,i,t)) is then made forthe subject.

The basal insulin sensitivity estimate (ISF_(basal,i,t)) is made for thesubject upon occurrence of a first fasting event undertaken by thesubject within a period of time encompassed by the first data set, whenthe first fasting event is deemed qualified. In some embodiments, thefirst fasting event is deemed qualified when (i) the subject has takenno correction bolus of the short acting insulin medicament in the twelvehours prior to the first fasting event and (ii) the subject has taken ameal bolus of the short acting insulin medicament with eachhypoglycaemic event free meal in the fourteen hours prior to the firstfasting event, e.g, a meal bolus was taken with each meal except if thebolus was not taken due to a hypoglyceamic event. The basal insulinsensitivity estimate (ISF_(basal,i,t)) makes use of (i) an expectedfasting blood glucose level based upon a present dosing of a long actinginsulin medicament in the long acting insulin medicament regimen(FBG_(expected)) during the first fasting event, (ii) a fasting glucoselevel of the subject during the first fasting event (

_(i)) that is obtained from the portion of the plurality of glucosemeasurements that is contemporaneous with the first fasting event, and(iii) an insulin sensitivity factor of the subject during a qualifiedfasting event occurring before the first fasting event(ISF_(basal,i-p,t)). In some embodiments, the basal insulin sensitivityestimate (ISF_(basal, i,t)) for the subject is computed as:

ISF basal , i , t = ( FBG expected - i i + 1 )  ISF basal , i - p , t .

A basal insulin sensitivity factor curve (ISF_(basal,i)) is estimatedwhen the new basal insulin sensitivity estimate (ISF_(basal,i,t)) ismade. Whereas the new basal insulin sensitivity factor estimate(ISF_(basal,i,t)) represents basal insulin sensitivity of the subject atthe time of the occurrence of the first qualified fasting event, thebasal insulin sensitivity factor curve estimate (ISF_(basal,i))represents the basal insulin sensitivity factor of the subject over apredetermined recurring time period, such as the course of a day.However, the new basal insulin sensitivity estimate (ISF_(basal,i,t)) isused to update the basal insulin sensitivity factor curve estimate(ISF_(basal,i)) in accordance with the teachings of the presentdisclosure. In some embodiments, the basal sensitivity factor curveestimate (ISF_(basal,i)) is computed by the formula:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

where ISF_(basal,i-p) represents a prior basal sensitivity factor curveestimate and, here, t serves to index through the entire basalsensitivity factor curve.

The bolus insulin sensitivity estimate (ISF_(bolus,i,t)) is made for thesubject upon occurrence of a correction bolus with a short actinginsulin medicament within the period of time. This estimate makes use of(i) an expected blood glucose level based upon the correction bolus withthe short acting insulin medicament (BG_(expected)), (ii) the glucoselevel of the subject after occurrence of the correction bolus (

_(corr,i)), where

_(corr,i) is obtained from the portion of the plurality of glucosemeasurements that is contemporaneous with a period of time after theoccurrence of the correction bolus, and (iii) an insulin sensitivityfactor of the subject estimated based upon occurrence of a priorcorrection bolus with the short acting insulin medicament(ISF_(bolus,i-p,t)). In some embodiments, this bolus insulin sensitivityestimate (ISF_(bolus,i,t)) is computed as:

ISF bolus , i , t = ( BG expected - corr , i corr , i + 1 )  ISF bolus, i - p .

A bolus insulin sensitivity factor curve (ISF_(bolus,i)) is made whenthe new bolus insulin sensitivity estimate (ISF_(bolus,i,t)) is made.Whereas the new bolus insulin sensitivity estimate (ISF_(bolus,i,t))represents bolus insulin sensitivity of the subject at the time of thecorrection bolus, the bolus insulin sensitivity factor curve(ISF_(bolus,i)) represents the bolus insulin sensitivity factor of thesubject over a predetermined recurring time period, such as the courseof a day. However, the new bolus insulin sensitivity estimate(ISF_(bolus,i,t)) is used to update the bolus insulin sensitivity factorcurve (ISF_(bolus,i)) in accordance with the teachings of the presentdisclosure. In some embodiments, the estimating the bolus sensitivityfactor curve comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

where ISF_(bolus,i-p) represents a prior bolus sensitivity factor curveestimate.

In some embodiments, further estimates are made. For instance, in someembodiments the bolus insulin sensitivity curve (ISF_(bolus,i)) isestimated as a function of the newly estimated basal insulin sensitivityfactor curve (ISF_(basal,i)) That is, when the estimated basal insulinsensitivity factor curve (ISF_(basal,i)) is estimated as describedabove, the newly estimated basal insulin sensitivity factor curve(ISF_(basal,i)) is used to estimate the bolus insulin sensitivity curve(ISF_(bolus,i)). In some embodiments, the estimating of the bolusinsulin sensitivity curve (ISF_(bolus,i)) as a function of the estimatedbasal insulin sensitivity factor curve (ISF_(basal,i)) comprisescomputing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

where ISF_(bolus,i-p) represents a prior bolus sensitivity factor curveestimate.

Correspondingly, in some embodiments, the basal insulin sensitivitycurve (ISF_(basal,i)) is estimated as a function of the newly estimatedbolus insulin sensitivity factor curve (ISF_(bolus,i)). That is, whenthe estimated bolus insulin sensitivity factor curve (ISF_(bolus,i)) isestimated as described above, the newly estimated bolus insulinsensitivity factor curve (ISF_(bolus,i)) is used to estimate the basalinsulin sensitivity curve (ISF_(basal,i)) In some embodiments, theestimating the basal insulin sensitivity curve (ISF_(basal,i)) as afunction of the estimated bolus insulin sensitivity factor curve(ISF_(bolus,i)) comprises computing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

where ISF_(basal,i-p) represents a prior basal sensitivity factor curveestimate.

The above embodiments describe the computation of a new estimated bolusinsulin sensitivity curve (ISF_(bolus,i)) and/or a new basal insulinsensitivity curve (ISF_(basal,i)) for an i^(th) time period, such as ani^(th) day. Typically this i^(th) time period (e.g., this i^(th) day) isthe present day. In some embodiments, when a new estimated bolus insulinsensitivity curve (ISF_(bolus,i)) has been estimated, it is thencombined with one or more bolus insulin sensitivity curve estimates fromprior days (or prior recurring time periods) in order to form an updatedbolus insulin sensitivity curve (ISF_(bolus)). In some embodiments, thisbolus insulin sensitivity factor curve updated by computing:

${{ISF}_{bolus} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{bolus},n}}}},$

where q is a predetermined number of historical updates to the bolusinsulin sensitivity curve (ISF_(bolus)), w is a linear or nonlinearvector of normalised weights, n is an integer index into the historicalupdates to ISF_(bolus) and vector w, and ISF_(bolus,n) is an n^(th) pastbolus insulin sensitivity curve (ISF_(bolus)).

Likewise, in some embodiments, when a new estimated basal insulinsensitivity factor curve (ISF_(basal,i)) has been made, it is thencombined with one or more basal insulin sensitivity curves from priordays (or prior recurring time periods) in order to form an updated basalinsulin sensitivity factor curve (ISF_(basal)) In some embodiments, theupdating the basal insulin sensitivity factor curve comprises computing:

${{ISF}_{basal} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{basal},n}}}},$

where q is a predetermined number of historical updates to the basalinsulin sensitivity curve (ISF_(basal)), w is a linear or nonlinearvector of normalised weights, n is an integer index into the historicalupdates to the basal insulin sensitivity curve (ISF_(basal)) and vectorw, and ISF_(basal,n) is an n^(th) past basal insulin sensitivity curve(ISF_(basal)) curve.

In some such embodiments, a recommended dose of the short acting insulinto achieve a target fasting glucose level in the subject is provided byusing glucose measurements from a portion of the plurality of glucosemeasurements and the updated bolus insulin sensitivity curve(ISF_(bolus)) or the updated basal insulin sensitivity curve(ISF_(basal)).

In some embodiments, the method further comprises obtaining a third dataset that comprises a plurality of physiological measurements of thesubject taken over the first period of time and, for each respectivephysiological measurement in the plurality of physiologicalmeasurements, a physiological measurement timestamp representing whenthe respective physiological measurement was made. In such embodiments,the value of p, in other words the amount of historical data that isused to update the basal and/or bolus insulin sensitivity factor curves,is determined by the plurality of physiological measurements. In someembodiments, each physiological measurement is a measurement of bodytemperature of the subject and the value p is reduced during periodswhen the subject has an elevated temperature. In some embodiments, eachphysiological measurement is a measurement of activity of the subjectand the value p is reduced during periods when the subject is incurringelevated activity.

In some embodiments, the long acting insulin medicament consists of asingle insulin medicament having a duration of action that is between 12and 24 hours or a mixture of insulin medicaments that collectively havea duration of action that is between 12 and 24 hours, and the shortacting insulin medicament consists of a single insulin medicament havinga duration of action that is between three to eight hours or a mixtureof insulin medicaments that collectively have a duration of action thatis between three to eight hours.

In another aspect is provided, a device for estimating parameters in aninsulin medicament regimen for a subject that includes both a shortacting insulin medicament regimen and a long acting insulin medicamentregimen, and wherein the device comprises one or more processors and amemory, the memory storing instructions that, when executed by the oneor more processors, perform a method of:

-   -   A) obtaining a first data set, the first data set comprising a        plurality of glucose measurements of the subject taken over a        first period of time and, for each respective glucose        measurement in the plurality of glucose measurements, a        timestamp representing when the respective measurement was made;    -   B) determining an estimate by:        -   B.1) making a basal insulin sensitivity estimate            (ISF_(basal,i,t)) for the subject upon occurrence of a first            fasting event undertaken by the subject within the first            period of time, when the first fasting event is deemed            qualified, the estimating using (i) an expected fasting            blood glucose level based upon a present dosing of a long            acting insulin medicament in the long acting insulin            medicament regimen (FBG_(expected)) during the first fasting            event, (ii) a fasting glucose level of the subject during            the first fasting event (            _(i)) that is obtained from the portion of the plurality of            glucose measurements that is contemporaneous with the first            fasting event, and (iii) an insulin sensitivity factor of            the subject during a qualified fasting event occurring            before the first fasting event (ISF_(basal,i-p,t); or)        -   B.2) making a bolus insulin sensitivity estimate            (ISF_(bolus,i,t)) for the subject upon occurrence of a            correction bolus with a short acting insulin medicament            within the first period of time, the estimating using (i) an            expected blood glucose level based upon the correction bolus            with the short acting insulin medicament            (BG_(expected)), (ii) the glucose level of the subject after            occurrence of the correction bolus (            _(corr,i)), wherein            _(corr,i) is obtained from the portion of the plurality of            glucose measurements that is contemporaneous with a period            of time after the occurrence of the correction bolus,            and (iii) an insulin sensitivity factor of the subject            estimated based upon occurrence of a prior correction bolus            with the short acting insulin medicament            (ISF_(bolus,i-p,t));    -   C) estimating a basal insulin sensitivity factor curve        (ISF_(basal,i)) when the bolus insulin sensitivity estimate        (ISF_(basal,i,t)) is made in B.1); and    -   D) estimating a bolus insulin sensitivity factor curve        (ISF_(bolus,i)) when the bolus insulin sensitivity estimate        (ISF_(bolus,i,t)) is made in B.2).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system topology that includes a regimenmonitoring device for estimating parameters in an insulin medicamentregimen, a regimen adherence assessor device for analyzing and preparingregimen adherence data, one or more glucose sensors that measure glucosedata from the subject, and one or more insulin pens that are used by thesubject to inject insulin medicaments in accordance with the prescribedinsulin medicament regimen, where the above-identified components areinterconnected, optionally through a communications network, inaccordance with an embodiment of the present disclosure.

FIG. 2 illustrates a device for estimating parameters in an insulinmedicament regimen for a subject in accordance with an embodiment of thepresent disclosure.

FIG. 3 illustrates a device for estimating parameters in an insulinmedicament regimen for a subject in accordance with another embodimentof the present disclosure.

FIGS. 4A, 4B, and 4C collectively provide a flow chart of processes andfeatures of a device for estimating parameters in an insulin medicamentregimen for a subject in accordance with various embodiments of thepresent disclosure.

FIG. 5 illustrates an example integrated system of connected insulinpen(s), continuous glucose monitor(s), memory and a processor forperforming algorithmic categorization of autonomous glucose data inaccordance with an embodiment of the present disclosure.

FIG. 6 illustrates basal insulin sensitivity estimate (ISF_(basal,i,t))and bolus insulin sensitivity estimate (ISF_(bolus,i,t)) data structuresin accordance with an embodiment of the present disclosure.

FIG. 7 illustrates a basal insulin sensitivity factor curve(ISF_(basal,i)) data structure and a bolus insulin sensitivity factorcurve (ISF_(bolus,i)) data structure in accordance with an embodiment ofthe present disclosure.

FIG. 8 illustrates a basal (long acting) insulin sensitivity factorcurve (ISF_(basal)) and a bolus (short acting) insulin sensitivityfactor curve (ISF_(bolus)) over the course of a time period, such as aday, in order to illustrate how the insulin sensitivity factor of asubject varies over this course of such a time period.

FIG. 9A illustrates determining a basal insulin sensitivity estimate(ISF_(basal,i,t)) upon occurrence of a qualified fasting event at agiven time (t) and how a value of this basal insulin sensitivityestimate (ISF_(basal,i,t)) compares to respective existing basal andbolus insulin sensitivity factor curves ISF_(basal) and ISF_(bolus) overthe course of a time period in accordance with an embodiment of thepresent disclosure.

FIG. 9B illustrates estimating a basal insulin sensitivity factor curve(ISF_(basal,i)) once a basal insulin sensitivity estimate(ISF_(basal,i,t)) has been made in accordance with an embodiment of thepresent disclosure.

FIG. 9C illustrates computation of a bolus insulin sensitivity curveestimate (ISF_(bolus,i)) as a function of the newly estimated basalinsulin sensitivity factor curve (ISF_(basal,i)) is accordance with anembodiment of the present disclosure.

FIG. 10A illustrates an example in which insulin sensitivity factorupdates for a subject are made based on correction bolus only.

FIG. 10B illustrates an example in which insulin sensitivity factorupdates are based on correction bolus and fasting glucose in accordancewith the methods set forth in FIG. 4, in accordance with an embodimentof the present disclosure.

FIG. 10C illustrates an example in which insulin sensitivity factorupdates are based on correction bolus and fasting glucose in accordancewith the methods set forth in FIG. 4, and in particular information fromthe wearable devices is used to make new ISF curve estimates weighhigher than past ISF curve estimates when updating the insulinsensitivity factor curve based upon past insulin sensitivity curveestimates in accordance with an embodiment of the present disclosure.

FIG. 10D illustrates an example in which insulin sensitivity factorupdates are as in FIG. 10C except that the carb-to-insulin ratio CIR isupdated proportionally to changes in ISF in accordance with anembodiment of the present disclosure.

FIG. 11 illustrates how the overall adherence of the subject to aninsulin medicament regimen is monitored and how the bolus insulinsensitivity curve estimation and updating is suspended during periods oftime when the overall adherence of the subject to an insulin medicamentregimen falls below a threshold value.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

The present disclosure relies upon the acquisition of data regarding aplurality of metabolic events, such as fasting events or meals, asubject engaged in over a period of time. For each such metabolic event,the data includes a timestamp. FIG. 1 illustrates an example of a system48 for estimating parameters in an insulin medicament regimen for asubject and an integrated system 502 for the acquisition of such data,and FIG. 5 provides more details of such a system 502. The integratedsystem 502 includes one or more connected insulin pens 104, one or morecontinuous glucose monitors 102, and a memory 506.

With the integrated system 502, autonomous timestamped glucosemeasurements of the subject are obtained 520. Also, data from the one ormore insulin pens used to apply a prescribed insulin regimen to thesubject is obtained 540 as a plurality of records. Each record comprisesa timestamped event specifying an amount of injected insulin medicamentthat the subject received as part of the prescribed insulin medicamentdosage regimen. In some embodiments, fasting events are identified usingthe autonomous timestamped glucose measurements of the subject.Optionally, meal events are also identified using the autonomoustimestamped glucose measurements 520 In this way, the glucosemeasurements are categorized 501 and filtered 504 and stored innon-transitory memory 506. This filtered glucose data is communicated inaccordance with the methods of the present disclosure 508. For instance,in the form of fasting events, time stamped glucose measurements, andbolus correction events autonomously identified due to their temporalproximity to autonomously determined meal events.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first subject could be termed asecond subject, and, similarly, a second subject could be termed a firstsubject, without departing from the scope of the present disclosure. Thefirst subject and the second subject are both subjects, but they are notthe same subject. Furthermore, the terms “subject” and “user” are usedinterchangeably herein. By the term insulin pen is meant an injectiondevice suitable for applying discrete doses of insulin, and wherein theinjection device is adapted for logging and communicating dose relateddata.

The terminology used in the present disclosure is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used in the description of the inventionand the appended claims, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will also be understood that the term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

A detailed description of a system 48 for estimating parameters in aninsulin medicament regimen for a subject that includes both a shortacting insulin medicament regimen and a long acting insulin medicamentregimen in accordance with the present disclosure is described inconjunction with FIGS. 1 through 3. As such, FIGS. 1 through 3collectively illustrate the topology of the system in accordance withthe present disclosure. In the topology, there is an insulin medicamentdosage regimen monitoring device (“monitoring device 250”) (FIGS. 1, 2,and 3), a data processing device (“processing device 200”), one or moreglucose sensors 102 associated with the subject (FIG. 1), and one ormore insulin pens 104 for injecting insulin medicaments into the subject(FIG. 1). Throughout the present disclosure, the processing device 200and the monitoring device 250 will be referenced as separate devicessolely for purposes of clarity. That is, the disclosed functionality ofthe processing device 200 and the disclosed functionality of themonitoring device 250 are contained in separate devices as illustratedin FIG. 1. However, it will be appreciated that, in fact, in someembodiments, the disclosed functionality of the processing device 200and the disclosed functionality of the monitoring device 250 arecontained in a single device. In some embodiments, the disclosedfunctionality of the processing device 200 and/or the disclosedfunctionality of the monitoring device 250 are contained in a singledevice and this single device is a glucose monitor 102 or the insulinpen 104.

Referring to FIG. 1, the monitoring device 250 estimates parameters inan insulin medicament regimen for a subject that includes both a shortacting insulin medicament regimen and a long acting insulin medicamentregimen. To do this, the processing device 200, which is in electricalcommunication with the monitor device 250, receives autonomous glucosemeasurements originating from one or more glucose sensors 102 attachedto a subject on an ongoing basis. Further, the processing device 200receives insulin medicament injection data from one or more insulin pens104 used by the subject to obtain insulin medicaments. In someembodiments, the processing device 200 receives such data directly fromthe glucose sensor(s) 102 and insulin pens 104 used by the subject. Forinstance, in some embodiments the processing device 200 receives thisdata wirelessly through radio-frequency signals. In some embodimentssuch signals are in accordance with an 802.11 (WiFi), Bluetooth, orZigBee standard. In some embodiments, the monitoring device 200 receivessuch data directly, identifies metabolic events, such as fasting eventsand meals, within the data, and passes such data to the monitoringdevice 250. In some embodiments the glucose sensor 102 and/or insulinpen includes an RFID tag and communicates to processing device 200and/or the monitoring device 250 using RFID communication.

In some embodiments, the processing device 200 and/or the monitoringdevice 250 is not proximate to the subject and/or does not have wirelesscapabilities or such wireless capabilities are not used for the purposeof acquiring glucose data and insulin medicament injection data. In suchembodiments, a communication network 106 may be used to communicateglucose measurements from the one or more glucose sensors 102 to theprocessing device 200 and from the one or more insulin pens 104 to theprocessing device 200.

Examples of networks 106 include, but are not limited to, the World WideWeb (WWW), an intranet and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN) and/or ametropolitan area network (MAN), and other devices by wirelesscommunication. The wireless communication optionally uses any of aplurality of communications standards, protocols and technologies,including but not limited to Global System for Mobile Communications(GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packetaccess (HSDPA), high-speed uplink packet access (HSUPA), Evolution,Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long termevolution (LTE), near field communication (NFC), wideband code divisionmultiple access (W-CDMA), code division multiple access (CDMA), timedivision multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi)(e.g., IEEE 802.11a, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11b, IEEE802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP),Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol(IMAP) and/or post office protocol (POP)), instant messaging (e.g.,extensible messaging and presence protocol (XMPP), Session InitiationProtocol for Instant Messaging and Presence Leveraging Extensions(SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or ShortMessage Service (SMS), or any other suitable communication protocol,including communication protocols not yet developed as of the filingdate of the present disclosure.

In some embodiments, there is a single glucose sensor 102 attached tothe subject and the processing device 200 and/or the monitoring device250 is part of the glucose sensor 102. That is, in some embodiments, theprocessing device 200 and/or the monitoring device 250 and the glucosesensor 102 are a single device.

In some embodiments, the adherence device 200 and/or the monitor device250 is part of an insulin pen or pump 104. That is, in some embodiments,the adherence device 200 and/or the monitor device 250 and an insulinpen 104 are a single device.

Of course, other topologies of the system 48 are possible. For instance,rather than relying on a communications network 106, the one or moreglucose sensors 102 and the one or more insulin pens 104 may wirelesslytransmit information directly to the processing device 200 and/or themonitoring device 250. Further, in some embodiments, the processingdevice 200 and/or the monitoring device 250 constitutes a portableelectronic device, a server computer, or in fact constitute severalcomputers that are linked together in a network or be a virtual machinein a cloud computing context. As such, the exemplary topology shown inFIG. 1 merely serves to describe the features of an embodiment of thepresent disclosure in a manner that will be readily understood to one ofskill in the art.

Referring to FIG. 2, in typical embodiments, the monitoring device 250comprises one or more computers. For purposes of illustration in FIG. 2,the monitoring device 250 is represented as a single computer thatincludes all of the functionality for evaluating historical adherence toa prescribed insulin medicament dosage regimen for a subject. However,the disclosure is not so limited. In some embodiments, the functionalityfor estimating parameters in an insulin medicament regimen for a subjectthat includes both a short acting insulin medicament regimen and a longacting insulin medicament regimen is spread across any number ofnetworked computers and/or resides on each of several networkedcomputers and/or is hosted on one or more virtual machines at a remotelocation accessible across the communications network 106. One of skillin the art will appreciate that any of a wide array of differentcomputer topologies are used for the application and all such topologiesare within the scope of the present disclosure.

Turning to FIG. 2 with the foregoing in mind, an exemplary monitoringdevice 250 for estimating parameters in an insulin medicament regimenfor a subject comprises one or more processing units (CPU's) 274, anetwork or other communications interface 284, a memory 192 (e.g.,random access memory), one or more magnetic disk storage and/orpersistent devices 290 optionally accessed by one or more controllers288, one or more communication busses 212 for interconnecting theaforementioned components, and a power supply 276 for powering theaforementioned components. In some embodiments, data in memory 192 isseamlessly shared with non-volatile memory 290 using known computingtechniques such as caching. In some embodiments, memory 192 and/ormemory 290 includes mass storage that is remotely located with respectto the central processing unit(s) 274. In other words, some data storedin memory 192 and/or memory 290 may in fact be hosted on computers thatare external to the monitoring device 250 but that can be electronicallyaccessed by the monitoring device 250 over an Internet, intranet, orother form of network or electronic cable (illustrated as element 106 inFIG. 2) using network interface 284.

The memory 192 of the monitoring device 250 for estimating parameters inan insulin medicament regimen for a subject stores:

-   -   an operating system 202 that includes procedures for handling        various basic system services;    -   an insulin regimen monitoring module 204;    -   an insulin medicament regimen for a subject, the insulin        medicament regimen comprising a long acting insulin medicament        regimen 208 and a short acting insulin medicament regimen 214;    -   a first data set 220, the first data set comprising a plurality        of glucose measurements for the subject and, for each respective        glucose measurement 222 in the plurality of glucose        measurements, a timestamp 224 representing when the respective        glucose measurement was made;    -   a second data set (not shown) from one or more insulin pens used        by the subject to apply the insulin medicament dosage regimen,        the second data set comprises a plurality of insulin medicament        records, each insulin medicament record in the plurality of        medicament records comprises: (i) a respective insulin        medicament injection event including an amount of insulin        medicament injected into the subject using a respective insulin        pen in the one or more insulin pens (ii) a corresponding        electronic timestamp that is automatically generated by the        respective insulin pen upon occurrence of the respective insulin        medicament injection event, and (iii) a type of insulin        medicament, wherein the type of insulin medicament is a short        acting insulin medicament or a long acting insulin medicament;    -   a basal insulin sensitivity estimate (ISF_(basal,i,t)) 230 for        the subject;    -   bolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232 for the        subject;    -   a basal insulin sensitivity factor curve estimate        (ISF_(basal,i)) 234 for the subject;    -   a bolus insulin sensitivity factor curve estimate        (ISF_(bolus,i)) 236 for the subject; and    -   an optional third data set 220, the third data set comprising a        plurality of physiological measurements for the subject and, for        each respective physiological measurement 240 in the plurality        of physiological measurements, a timestamp 242 representing when        the respective physiological measurement was made.

FIG. 6 provides further details on a basal insulin sensitivity estimate(ISF_(basal,i,t)) 230 for a subject. Each basal insulin sensitivityestimate (ISF_(basal,i,t)) 230 represents a basal insulin sensitivityestimate 230-x-y, that is taken on a particular day 602-x, at aparticular time t 604-x-y, where, here, x represents the particular day,and y represents the particular time t. FIG. 6 also provides furtherdetails on a bolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232for a subject. Each bolus insulin sensitivity estimate (ISF_(bolus,i,t))232 represents a bolus insulin sensitivity estimate 232-x-y, that istaken on a particular day 606-x, at a particular time t 608-x-y, where,here, x represents the particular day, and y represents the particulartime t.

FIG. 7 provides further details on a basal insulin sensitivity curveestimate (ISF_(basal,i)) 234 for a subject. The basal insulinsensitivity curve estimate (ISF_(basal,i)) 234 includes a basal insulinsensitivity estimate 612-y for each time t 610-y across a particularrecurring time period, such as a day, a week or a month. In someembodiments, two or more, three or more, or five or more basal insulinsensitivity curve estimates (ISF_(basal,i)) 234 are combined to form anupdated basal insulin sensitivity curve in accordance with the presentdisclosure. FIG. 7 also provides further details on a bolus insulinsensitivity curve estimate (ISF_(bolus,i)) 236 for a subject. The bolusinsulin sensitivity curve estimate (ISF_(bolus,i)) 236 includes a bolusinsulin sensitivity estimate 616-y for each time t 614-y across aparticular recurring time period, such as a day, a week or a month. Insome embodiments, two or more, three or more, or five or more bolusinsulin sensitivity curve estimates (ISF_(bolus,i)) 236 are combined toform an updated bolus insulin sensitivity curve in accordance with thepresent disclosure.

In some embodiments, the insulin regimen monitoring module 204 isaccessible within any browser (phone, tablet, laptop/desktop). In someembodiments the insulin regimen monitoring module 204 runs on nativedevice frameworks, and is available for download onto the monitoringdevice 250 running an operating system 202 such as Android or iOS.

In some implementations, one or more of the above identified dataelements or modules of the monitoring device 250 for estimatingparameters in an insulin medicament dosage regimen are stored in one ormore of the previously described memory devices, and correspond to a setof instructions for performing a function described above. Theabove-identified data, modules or programs (e.g., sets of instructions)need not be implemented as separate software programs, procedures ormodules, and thus various subsets of these modules may be combined orotherwise re-arranged in various implementations. In someimplementations, the memory 192 and/or 290 optionally stores a subset ofthe modules and data structures identified above. Furthermore, in someembodiments the memory 192 and/or 290 stores additional modules and datastructures not described above.

In some embodiments, a monitoring device 250 for estimating parametersin an insulin medicament dosage regimen is a smart phone (e.g., aniPHONE), laptop, tablet computer, desktop computer, or other form ofelectronic device (e.g., a gaming console). In some embodiments, themonitoring device 250 is not mobile. In some embodiments, the monitoringdevice 250 is mobile.

FIG. 3 provides a further description of a specific embodiment of amonitoring device 250 in accordance with the instant disclosure. Themonitoring device 250 illustrated in FIG. 3 has one or more processingunits (CPU's) 274, peripherals interface 370, memory controller 368, anetwork or other communications interface 284, a memory 192 (e.g.,random access memory), a user interface 278, the user interface 278including a display 282 and input 280 (e.g., keyboard, keypad, touchscreen), an optional accelerometer 317, an optional GPS 319, optionalaudio circuitry 372, an optional speaker 360, an optional microphone362, one or more optional intensity sensors 364 for detecting intensityof contacts on the monitor device 250 (e.g., a touch-sensitive surfacesuch as a touch-sensitive display system 282 of the monitor device 250),an optional input/output (I/O) subsystem 366, one or more optionaloptical sensors 373, one or more communication busses 212 forinterconnecting the aforementioned components, and a power supply 276for powering the aforementioned components.

In some embodiments, the input 280 is a touch-sensitive display, such asa touch-sensitive surface. In some embodiments, the user interface 278includes one or more soft keyboard embodiments. The soft keyboardembodiments may include standard (QWERTY) and/or non-standardconfigurations of symbols on the displayed icons.

The monitoring device 250 illustrated in FIG. 3 optionally includes, inaddition to accelerometer(s) 317, a magnetometer (not shown) and a GPS319 (or GLONASS or other global navigation system) receiver forobtaining information concerning the location and orientation (e.g.,portrait or landscape) of the monitoring device 250 and/or fordetermining an amount of physical exertion by the subject or otherphysiological measurements 240.

It should be appreciated that the monitoring device 250 illustrated inFIG. 3 is only one example of a multifunction device that may be usedfor estimating parameters in an insulin medicament dosage regimen for asubject, and that the monitoring device 250 optionally has more or fewercomponents than shown, optionally combines two or more components, oroptionally has a different configuration or arrangement of thecomponents. The various components shown in FIG. 3 are implemented inhardware, software, firmware, or a combination thereof, including one ormore signal processing and/or application specific integrated circuits.

Memory 192 of the monitoring device 250 illustrated in FIG. 3 optionallyincludes high-speed random access memory and optionally also includesnon-volatile memory, such as one or more magnetic disk storage devices,flash memory devices, or other non-volatile solid-state memory devices.Access to memory 192 by other components of the monitoring device 250,such as CPU(s) 274 is, optionally, controlled by the memory controller368.

The peripherals interface 370 can be used to couple input and outputperipherals of the device to CPU(s) 274 and memory 192. The one or moreprocessors 274 run or execute various software programs and/or sets ofinstructions stored in memory 192, such as the insulin regimenmonitoring module 204, to perform various functions for the monitoringdevice 250 and to process data.

In some embodiments, the peripherals interface 370, CPU(s) 274, andmemory controller 368 are, optionally, implemented on a single chip. Insome other embodiments, they are, optionally, implemented on separatechips.

RF (radio frequency) circuitry of network interface 284 receives andsends RF signals, also called electromagnetic signals. In someembodiments, the insulin medicament regimen 206, the first data set 220,and/or the third data set 238 is received using this RF circuitry fromone or more devices such as a glucose sensor 102 associated with asubject, an insulin pen 104 associated with the subject and/or theprocessing device 200. In some embodiments, the RF circuitry 108converts electrical signals to/from electromagnetic signals andcommunicates with communications networks and other communicationsdevices, glucose sensors 102, insulin pens 104, and/or the dataprocessing device 200 via the electromagnetic signals. The RF circuitry284 optionally includes well-known circuitry for performing thesefunctions, including but not limited to an antenna system, an RFtransceiver, one or more amplifiers, a tuner, one or more oscillators, adigital signal processor, a CODEC chipset, a subscriber identity module(SIM) card, memory, and so forth. The RF circuitry 284 optionallycommunicates with the communication network 106. In some embodiments,the circuitry 284 does not include RF circuitry and, in fact, isconnected to the network 106 through one or more hard wires (e.g., anoptical cable, a coaxial cable, or the like).

In some embodiments, the audio circuitry 372, the optional speaker 360,and the optional microphone 362 provide an audio interface between thesubject and the monitoring device 250. The audio circuitry 372 receivesaudio data from the peripherals interface 370, converts the audio datato electrical signals, and transmits the electrical signals to thespeaker 360. The speaker 360 converts the electrical signals tohuman-audible sound waves. The audio circuitry 372 also receiveselectrical signals converted by the microphone 362 from sound waves. Theaudio circuitry 372 converts the electrical signal to audio data andtransmits the audio data to the peripherals interface 370 forprocessing. The audio data is, optionally, retrieved from and/ortransmitted to the memory 192 and/or the RF circuitry 284 by theperipherals interface 370.

In some embodiments, the power supply 276 optionally includes a powermanagement system, one or more power sources (e.g., battery, alternatingcurrent (AC)), a recharging system, a power failure detection circuit, apower converter or inverter, a power status indicator (e.g., alight-emitting diode (LED)) and any other components associated with thegeneration, management and distribution of power in portable devices.

In some embodiments, the monitoring device 250 optionally also includesone or more optical sensors 373. The optical sensor(s) 373 optionallyinclude charge-coupled device (CCD) or complementary metal-oxidesemiconductor (CMOS) phototransistors. The optical sensor(s) 373 receivelight from the environment, projected through one or more lens, andconverts the light to data representing an image. The optical sensor(s)373 optionally capture still images and/or video. In some embodiments,an optical sensor is located on the back of the monitoring device 250,opposite the display 282 on the front of the device 250, so that theinput 280 is enabled for use as a viewfinder for still and/or videoimage acquisition. In some embodiments, another optical sensor 373 islocated on the front of the monitoring device 250 so that the subject'simage is obtained (e.g., to verify the health or condition of thesubject, to determine the physical activity level of the subject, tohelp diagnose a subject's condition remotely, to acquire physiologicalmeasurements 240, etc.).

As illustrated in FIG. 3, a monitoring device 250 preferably comprisesan operating system 202 that includes procedures for handling variousbasic system services. The operating system 202 (e.g., iOS, DARWIN,RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system suchas VxWorks) includes various software components and/or drivers forcontrolling and managing general system tasks (e.g., memory management,storage device control, power management, etc.) and facilitatescommunication between various hardware and software components.

In some embodiments the monitoring device 250 is a smart phone. In otherembodiments, the monitoring device 250 is not a smart phone but ratheris a tablet computer, desktop computer, emergency vehicle computer, orother form or wired or wireless networked device. In some embodiments,the monitoring device 250 has any or all of the circuitry, hardwarecomponents, and software components found in the monitoring device 250depicted in FIG. 2 or 3. In the interest of brevity and clarity, only afew of the possible components of the monitoring device 250 are shown inorder to better emphasize the additional software modules that areinstalled on the monitoring device 250.

While the system 48 disclosed in FIG. 1 can work standalone, in someembodiments it can also be linked with electronic medical records toexchange information in any way.

Now that details of a system 48 for estimating parameters in an insulinmedicament dosage regimen 206 have been disclosed, details regarding aflow chart of processes and features of the system, in accordance withan embodiment of the present disclosure, are disclosed with reference toFIGS. 4A through 4C. In some embodiments, these processes are carriedout by the monitoring device 250 described above, which comprises one ormore processors 274 and a memory 192/290. The memory stores instructionsthat, when executed by the one or more processors, perform the processesdisclosed with reference to FIGS. 4A through 4C and that are describedbelow. In some embodiments, such processes and features of the systemare carried out by the insulin regimen monitoring module 204 illustratedin FIGS. 2 and 3.

Block 402. With reference to block 402 of FIG. 4A, the goal of manyinsulin therapies is to match as closely as possible normal physiologicinsulin secretion to control fasting and postprandial plasma glucose.This is done with an insulin medicament regimen 206 for the subject. Inthe present disclosure, the insulin medicament regimen 206 comprises along acting insulin medicament regimen 208 and a short acting insulinmedicament regimen 214. In the disclosure, the insulin sensitivityfactor (ISF) and the carb-to-insulin ratio (CIR) are updated based onthe response of a subject to fast and long acting insulin medicamentinjection events. Two parameters of an insulin medicament regimen 206that can be used in dosage calculations for long and fast acting insulinmedicaments are defined herein as the basal insulin sensitivity factorcurve ISF_(basal) and the bolus insulin sensitivity factor curveISF_(bolus). These two parameters are different for each subject andthey describe the sensitivity of a respective subject to long and fastacting insulin medicaments, and are proportional to each other. The ISFsensitivity can be different for different times of the day, asillustrated in FIG. 8, and this is why the ISF sensitivity is expressedas ISF curves. Moreover, as seen in FIG. 8, ISF_(basal) 804 andISF_(bolus) 806 each vary throughout the day but are proportional toeach other throughout the day. The present disclosure advantageouslytakes advantage of this proportionality between ISF_(basal) 804 andISF_(bolus) 806 to provide improved, more accurate ISF curves.

In some embodiments, the short acting insulin medicament used in theshort acting insulin medicament regimen 214 consists of a single insulinmedicament having a duration of action that is between three to eighthours or a mixture of insulin medicaments that collectively have aduration of action that is between three to eight hours. Examples ofsuch short acting insulin medicaments include, but are not limited, toLispro (HUMALOG, May 18, 2001, insulin lispro [rDNA origin] injection,[prescribing information], Indianapolis, Ind.: Eli Lilly and Company),Aspart (NOVOLOG, July 2011, insulin aspart [rDNA origin] injection,[prescribing information], Princeton, N.J., NOVO NORDISK Inc., July,2011), Glulisine (Helms Kelley, 2009, “Insulin glulisine: an evaluationof its pharmacodynamic properties and clinical application,” AnnPharmacother 43:658-668), and Regular (Gerich, 2002, “Novel insulins:expanding options in diabetes management,” Am J Med. 113:308-316).

In some embodiments, the long acting insulin medicament used in the longacting insulin medicament regimen 208 consists of a single insulinmedicament having a duration of action that is between 12 and 24 hoursor a mixture of insulin medicaments that collectively have a duration ofaction that is between 12 and 24 hours. In some embodiments, long actinginsulin medicaments suitable for use in the long acting insulinmedicament regimen 208 are those insulin medicaments having a durationof action that is between 12 and 24 hours or a mixture of insulinmedicaments that collectively have a duration of action that is between12 and 24 hours. Examples of such long acting insulin medicamentsinclude, but are not limited to, Insulin Degludec (developed by NOVONORDISK under the brand name Tresiba), NPH (Schmid, 2007, “New optionsin insulin therapy. J Pediatria (Rio J). 83(Suppl 5):S146-S155),Glargine (LANTUS, Mar. 2, 2007, insulin glargine [rDNA origin]injection, [prescribing information], Bridgewater, N.J.:Sanofi-Aventis), and Determir (Plank et al., 2005, “A double-blind,randomized, dose-response study investigating the pharmacodynamic andpharmacokinetic properties of the long-acting insulin analog detemir,”Diabetes Care 28:1107-1112).

In the method, a first data set 220 is obtained. The first data setcomprises a plurality of glucose measurements of the subject taken overa first period of time and, for each respective glucose measurement inthe plurality of glucose measurements, a timestamp representing when therespective measurement was made. In some embodiments, each glucosemeasurement 222 is an autonomous glucose measurement. The FREESTYLELIBRE CGM by ABBOTT (“LIBRE”) is an example of a glucose sensor that maybe used as a glucose sensor 102 that makes autonomous glucosemeasurements. The LIBRE allows calibration-free glucose measurementswith an on-skin coin-sized sensor, which can send up to eight hours ofdata to a reader device (e.g., the processing device 200 and/or themonitoring device 250) via near field communications, when brought closetogether. The LIBRE can be worn for fourteen days in all daily lifeactivities.

In the method, a second data set is obtained from one or more insulinpens used by the subject to apply the insulin medicament dosage regimen,the second data set comprises a plurality of insulin medicament records,each insulin medicament record in the plurality of medicament recordscomprises: (i) a respective insulin medicament injection event includingan amount of insulin medicament injected into the subject using arespective insulin pen in the one or more insulin pens (ii) acorresponding electronic timestamp that is automatically generated bythe respective insulin pen upon occurrence of the respective insulinmedicament injection event, and (iii) a type of insulin medicament,wherein the type of insulin medicament is a short acting insulinmedicament or a long acting insulin medicament

The first and second data set systematically provides timestamped data,and thereby contributes to the reliability of the estimated parameters,and the robustness of the estimator. If for example a change in insulinsensitivity is not detected by the ISF estimator, the next ISFcalculation will be less correct, as a new estimate is based on aprevious estimate. On the contrary, an efficient detection of a changein ISF will increase the validity of the estimated parameters.Therefore, the frequency of glucose measurements influences the abilityto resolve metabolic events that can be identified in the blood glucose,and thereby identify insulin related events. As the injection data isprovided directly by one or more insulin pens, the quality of theestimated parameters increases, and the systematicly obtained dataenables further adherence categorization based on stamped time-insulininjections. Therefore, ISF estimator utilizes the benefit of insulindose and glucose data systematically provided an insulin injectiondevice and glucose measuring device.

In some embodiments, the first data set comprises autonomous glucosemeasurements that are taken from the subject at an interval rate of 5minutes or less, 3 minutes or less, or 1 minute or less. However, thepresent disclosure is not limited to the use of first data sets 220 thatcomprises autonomous glucose measurements. In some embodiments, thefirst data set 220 comprises nonautonomous glucose measurements or acomposite of autonomous and nonautonomous glucose measurements

Blocks 406 and 408. In the present disclosure, when a fasting event hasoccurred (406-Yes), process control turns to steps 410 and 416 of FIG.4B. On the other hand, when a correction bolus injection event hasoccurred (408-Yes), process control turns to steps 428 and 432 of FIG.4C. In other instances, where a fasting event has not occurred (406-No),or a correction bolus has not occurred (408-No) process control waitsuntil a fasting event or a bolus correction is detected. In someinstances where the subject exhibits poor adherence to the insulinmedicament regimen, process control suspends altogether until adherenceto the insulin medicament regimen improves.

In some embodiments, the fasting event is detected autonomously using afasting detection algorithm and the glucose measurements in the firstdata set 220. There are a number of methods for detecting a fastingevent using glucose measurements 222 from a glucose monitor 102. Forinstance, in some embodiments a first fasting event is identified in afirst time period (e.g., a period of 24 hours) encompassed by theplurality of glucose measurements in the first data set 220 by firstcomputing a moving period of variance across the glucose measurements,where:

$\sigma_{k}^{2} = \left( {\frac{1}{M}{\sum\limits_{i = {k - M}}^{k}\left( {G_{i} - \overset{\_}{G}} \right)}} \right)^{2}$

and where, G, is the i^(th) glucose measurement in the portion k of theplurality of glucose measurements considered, M is a number of glucosemeasurements in the plurality of glucose measurements and represents acontiguous predetermined time span, G is the mean of the M glucosemeasurements selected from the plurality of glucose measurements of thefirst data set 220, and k is within the first time period. As anexample, the glucose measurements may span several days or weeks, withglucose measurements taken every five minutes. A first time period k(e.g., one day) within this overall time span is selected and thus theportion k of the plurality of measurements is examined for a period ofminimum variance. The first fasting period is deemed to be the period ofminimum variance

$\begin{matrix}\min \\k\end{matrix}\sigma_{k}^{2}$

within the first time period. Next, the process is repeated with portionk of the plurality of glucose measurements by examining the next portionk of the plurality of glucose measurements for another period of minimumvariance thereby assigning another fasting period.

Turning to block 408 of FIG. 4A, in some embodiments, the correctionbolus injection event is determined by pen injection data received fromthe one or more insulin pens 104. In some embodiments, a bolus injectionevent is determined by mapping the pen injection data received from theone or more insulin pens 104 onto meal events that are autonomouslyderived by analysis of the glucose measurements 222 in the first dataset 220. In such embodiments, when a pen injection event occurs shortlybefore or after a meal, it is deemed to be a bolus injection event. Insome embodiments, for this purpose, a meal event is detected from theglucose measurements 222 in the first data set 220 by computing: (i) afirst model comprising a backward difference estimate of glucose rate ofchange using the glucose measurements 222, (ii) a second modelcomprising a backward difference estimate of glucose rate of changebased on Kalman filtered estimates of glucose using the glucosemeasurements 222, (iii) a third model comprising a Kalman filteredestimate of glucose and Kalman filtered estimate of rate of change (ROC)of glucose based on the plurality of glucose measurements 222, and/or(iv) a fourth model comprising a Kalman filtered estimate of rate ofchange of ROC of glucose based on the glucose measurements 222. In somesuch embodiments, the first model, the second model, the third model andthe fourth model are each computed across the glucose measurements 222and a meal event is identified at an instance where at least three ofthe four models indicate a meal event. For further disclosure on suchmeal event detection, see Dassau et al., 2008, “Detection of a MealUsing Continuous Glucose Monitoring,” Diabetes Care 31, pp. 295-300,which is hereby incorporated by reference. See also, Cameron et al.,2009, “Probabilistic Evolving Meal Detection and Estimation of MealTotal Glucose Appearance,” Journal of Diabetes Science and Technology3(5), pp. 1022-1030, which is hereby incorporated by reference.

In some embodiments, only those bolus injection events and those fastingevents that are deemed to be insulin medicament regimen 206 adherent areused for the basal insulin sensitivity estimates (ISF_(basal,i,t)) 230and the bolus insulin sensitivity estimates (ISF_(bolus,i,t)) 232. Inother words, in some embodiments, only those bolus injection events thatare deemed insulin medicament regimen 206 adherent will trigger thecondition 408-Yes. Moreover, only those fasting events that are deemedinsulin medication regimen 206 adherent will trigger the condition406-Yes. Example 1, below, illustrates a way in which a determination ismade as to whether a bolus injection event or a fasting event is insulinregimen adherent. Moreover, European Patent Application NumberEP16177080.5, entitled “Regimen Adherence Measure for Insulin TreatmentBase on Glucose Measurement and Insulin Pen Data,” filed Jun. 30, 2016,which is hereby incorporated by reference, discloses techniques foridentifying and classifying fasting events as adherent or nonadherent.In some embodiments, only those fasting events that are classified as“basal regimen adherent” in accordance with European Patent ApplicationNumber EP16177080.5 will trigger the condition 406-Yes in the presentdisclosure. Further, European Patent Application Number EP16177080.5,discloses techniques for identifying and classifying meal events as“bolus regimen adherent” or “bolus regimen nonadherent.” In someembodiments, only those bolus injection event that are associated withmeals that are classified as “bolus regimen adherent” in accordance withEuropean Patent Application Number EP16177080.5 will trigger thecondition 408-Yes in the present disclosure.

Block 410. Turning to block 410 of FIG. 4B, in the case where a firstqualified fasting event occurred (406-Yes), a basal insulin sensitivityestimate (ISF_(basal,i,t)) 230 is made using: (i) an expected fastingblood glucose level based upon a present dosing of a long acting insulinmedicament in the long acting insulin medicament regimen(FBG_(expected)) during the first fasting event, (ii) a fasting glucoselevel of the subject during the first fasting event (

₁) that is obtained from the portion of the plurality of glucosemeasurements that is contemporaneous with the first fasting event, and(iii) a basal insulin sensitivity factor of the subject during aqualified fasting event occurring before the first fasting event(ISF_(basal,i-p,t)).

Referring to block 412 of FIG. 4B, in some such embodiments, the basalinsulin sensitivity estimate (ISF_(basal,i,t)) 230 is computed as:

ISF basal , i , t = ( FBG expected - + 1 )  ISF basal , i - p , t .

FIG. 9A illustrates the change in the value for ISF_(basal,i,t) 230 dueto the new basal update computed in block 412 of FIG. 4B.

Computation of FGB_(expected). In some embodiments FBG_(expected)(expected fasting blood glucose level) is a fasting blood glucose levelthat is obtained based upon trusted information. That is, informationobtained about a subject during a period of time when the subject wasadherent with the long acting insulin medicament regimen 208, denotedhere as time period p. In some embodiments, FBG_(expected) is obtainedbased on the present dosing of the long acting insulin medicament forthe given time period in which the fasting event occurred (t), as setforth in the long acting insulin medicament regimen 208 (FIGS. 2 and 3),as well as the basal insulin sensitivity factor for the subject at time(t) obtained from the existing basal insulin sensitivity curve(ISF_(basal,i-1)) 904 of FIG. 9. This information is trusted because thecurrent fasting event has already been determined to occur during a timewhen the subject was insulin regimen adherent. In other embodiments,FBG_(expected) is calculated by looking up the glucose measurements 222of the subject in the first data set 220 for the date and period of timethat corresponds to the last insulin regimen adherent fasting period ofthe subject (e.g., the fasting period just prior to the fasting periodthat triggered the last instance of the condition 406-Yes of FIG. 4A).The below example illustrates one way in which a fasting event isqualified as insulin regimen adherent. Moreover, European PatentApplication Number EP16177080.5, entitled “Regimen Adherence Measure forInsulin Treatment Based on Glucose Measurement and Insulin Pen Data,”filed Jun. 30, 2016, which is hereby incorporated by reference,discloses techniques for identifying and classifying fasting events asadherent or nonadherent. In some embodiments,

_(ad) are the glucose measurements from the fasting event that isclassified as “basal regimen adherent” in accordance with EuropeanPatent Application Number EP16177080.5 that is prior in time to thefasting event that trigged the last instance of condition 406-Yes inFIG. 4A in the present disclosure. Thus, in some embodiments,FBG_(expected) is computed as:

FBG _(expected)=

_(ad) −ISF _(basal,i-p,t) ΔU _(basal)

where,

-   -   i indicates the current day,    -   t is the time of day,    -   _(ad) is a glucose measurement 222 of the subject        contemporaneous with the last qualified (e.g., insulin regimen        adherent) fasting event of the subject (could also be denoted        _(basal,i-p)), or some other measure of central tendency of a        plurality of glucose measurements 222 of the subject        contemporaneous with the last qualified fasting event of the        subject,    -   ISF_(basal,i-p,t) is the basal insulin sensitivity factor value        taken at time t from the basal insulin sensitivity estimate        curve (ISF_(basal,i-p)) (e.g., basal insulin sensitivity        estimate curve ISF_(basal,i-1) 908 of FIG. 9A when p is 1), at        time t, where p typically has the value one but may have some        greater value when the last fasting period (p=1) corresponds to        a period in which the subject was not insulin regimen adherent,

ΔU _(basal) =U _(basal,i) −U _(basal,ad),

-   -   U_(basal,i) is, in some embodiments, the dosage of the long        acting insulin medicament 210 for the current time period i        (e.g., the current epoch 212) specified by the long acting        insulin medicament regimen 208, and, in other embodiments, is        the amount of long acting insulin medicament 210 the subject has        actually taken in the current time period i, as determined from        injection event records from insulin pens 104, where it will be        appreciated that U_(basal,i) may be equivalently drawn from        either source when the instant fasting period that triggered        condition 406-A is long acting insulin regimen adherent, and    -   U_(basal,ad) is, in some embodiments, the dosage of the long        acting insulin medicament 210 for the time that is        contemporaneous with the last qualified (e.g., insulin regimen        adherent) fasting event of the subject, and, in other        embodiments, is the amount of long acting insulin medicament 210        the subject has actually taken in the time period p that is        contemporaneous with the last qualified (e.g., insulin regimen        adherent) fasting event of the subject, as determined from        injection event records from insulin pens 104, where it will be        appreciated that U_(basal,ad) may be equivalently drawn from        either source because it is from a period of time when the        subject was long acting insulin regimen adherent. U_(basal,ad)        could also be denoted U_(basal,i-p).

Calculation of (

_(i)). In some embodiments

_(i) is a fasting blood glucose measurement during the time (t) of thefasting event, which is obtained from the portion of the plurality ofglucose measurements in the first data set 220 that is contemporaneousin time (t) with the fasting event that triggered condition 406-Yes(first fasting event). In some embodiments, the fasting event ismeasured over a period of three or more minutes, five or more minutes,between five minutes and thirty minutes or some other period of time. Assuch, in some embodiments, there is more than one glucose measurement222 for the fasting event in the first data set 220. When this is thecase (

_(i)) is an average value, or some other measure of central tendency, ofthe plurality of glucose measurements 222 within the time period.

Calculation of ISF_(basal,i-p,t). The value ISF_(basal,i-p,t) is theprior basal insulin sensitivity factor of the subject during a qualifiedfasting event occurring before the present fasting event. The valueISF_(basal,i-p,t) can, for example, be obtained from sampling the valueat time (t) on the existing basal insulin sensitivity curve(ISF_(basal,i,t)) 230 of FIG. 9A. With p set to 1, meaning takeISF_(basal,i-p,t) from the prior recurring time period, such as theprior day, a value for ISF_(basal,i-1,t) 908 is obtained forISF_(basal,i-p,t). In this way, computation of the equation of block 412results in a value ISF_(basal,i,t) 230 that is illustrated in FIG. 9A.

Block 414. Referring to block 414 of FIG. 4B, in some embodiments, thefirst fasting event is deemed qualified when (i) the subject has takenno correction bolus of the short acting insulin medicament in a firstpredetermined period of time (e.g., twelve hours) prior to the firstfasting event and (ii) the subject has taken a meal bolus of the shortacting insulin medicament with each hypoglycaemic event free meal in asecond predetermined time (e.g. fourteen hours) prior to the firstfasting event. In some embodiments the first predetermined period oftime is six hours or greater, seven hours or greater, eight hours orgreater, nine hours or greater, ten hours or greater, eleven hours orgreater or twelve hours or greater. In some embodiments the secondpredetermined period of time is ten hours or greater, eleven hours orgreater, twelve hours or greater, thirteen hours or greater, fourteenhours or greater, or fifteen hours or greater.

Referring to block 416 of FIG. 4B and as illustrated in FIGS. 9A and 9B,once the basal insulin sensitivity estimate (ISF_(basal,i,t)) 230 hasbeen made, a basal insulin sensitivity factor curve (ISF_(basal,i)) 234is estimated. Whereas the new basal insulin sensitivity factor estimate(ISF_(basal,i,t)) 230 represents basal insulin sensitivity of thesubject at the time (t) of the occurrence of the first qualified fastingevent, the basal insulin sensitivity factor curve estimate(ISF_(basal,i)) 234 represents the basal insulin sensitivity factor ofthe subject over a predetermined recurring time period, such as thecourse of a day. However, the new basal insulin sensitivity estimate(ISF_(basal,i,t)) 230 is used to update the basal insulin sensitivityfactor curve estimate (ISF_(basal,i)) 234 in accordance with theteachings of the present disclosure. Referring to block 418, in someembodiments, the basal sensitivity factor curve estimate (ISF_(basal,i))234 is computed by the formula:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

where ISF_(basal,i-p) represents a prior basal sensitivity factor curveestimate. For example, if p is from the day prior to the new fastingevent and thus has the value 1, and if the basal sensitivity factorcurve estimate is over the course of a recurring 24 hour time periodsuch as a day, the basal sensitivity factor curve estimate(ISF_(basal,i)) 234 is computed by shifting the prior basal sensitivityfactor curve estimate (ISF_(basal,i-p)) 904 of FIGS. 9A and 9B by thedelta between the new basal insulin sensitivity estimate(ISF_(basal,i,t)) 230 obtained in block 410 or 412 of FIG. 4B, and thebasal insulin sensitivity estimate (ISF_(basal,i-1,t)) 908 that isobtained by sampling the prior basal insulin sensitivity factor curve(ISF_(basal,i-1)) 904 of FIG. 9A or 9B for time (t), where this delta isexpressed as:

$\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right)$

for each sampled time t.

As illustrated by the transition from FIGS. 9A and 9B, even though thenew basal insulin sensitivity estimate (ISF_(basal,i,t)) 230 for singletime point in the time period, it is used to proportionally shift theentire basal sensitivity factor curve estimate (ISF_(basal,i-1)) 904 inorder to calculate a new basal sensitivity factor curve estimate(ISF_(basal,i)) 234.

Referring to block 420 and FIG. 9C, in some embodiments, furtherestimates are made. For instance, in some embodiments the bolus insulinsensitivity curve (ISF_(bolus,i)) 236 is estimated as a function of thenewly estimated basal insulin sensitivity estimate (ISF_(basal,i,t))230. That is, when the basal insulin sensitivity estimate(ISF_(basal,i,t)) 230 is estimated as described above, it is used toestimate the bolus insulin sensitivity curve (ISF_(bolus,i)) 236.Referring to block 422 of FIG. 4B, in some embodiments, the estimatingof the bolus insulin sensitivity curve (ISF_(bolus,i)) 236 as a functionof the estimated basal insulin sensitivity factor estimate(ISF_(basal,i,t)) 230 comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

where ISF_(bolus,i-p) represents a prior bolus sensitivity factor curveestimate and here the value t is now used to step through the bolusentire sensitivity factor curve estimate (e.g., through the entirepredetermined period of the curve, such as a day). For example, if p isfrom the day prior to the new fasting event and thus has the value 1,and if the basal and bolus insulin sensitivity factor curve estimatesare both over the course of a recurring 24 hour time period such as aday, the bolus sensitivity factor curve estimate (ISF_(bolus,i)) 236 iscomputed by shifting the prior bolus sensitivity factor curve estimate(ISF_(bolus,i-p)) 906 of FIGS. 9B and 9C by the delta between the newbasal insulin sensitivity estimate (ISF_(basal,i,t)) 230, denoted(ISF_(basal,i,t)) 230 (where FIG. 9C now illustrates one of many sampledvalues (ISF_(basal,i,t)) 230), and the corresponding basal insulinsensitivity estimate (ISF_(basal,i-1,t)) 908 that is obtained bysampling the prior basal insulin sensitivity factor curve(ISF_(basal,i-1)) 904 of FIG. 9B or 9C for time (t), where this delta isexpressed as:

$\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right) = \left( \frac{{ISF}_{{basal},i}}{{ISF}_{{basal},{i - p}}} \right)$

for each sampled time t.

Referring to block 424 of FIG. 4B, the above embodiments describe thecomputation of a new estimated basal insulin sensitivity curve(ISF_(basal,i)) 234 for an i^(th) time period, such as an i^(th) day.Typically this i^(th) time period (e.g., this i^(th) day) is the presentday. In some embodiments, when a new estimated basal insulin sensitivitycurve (ISF_(bolus,i)) 234 has been made, it is then combined with one ormore basal insulin sensitivity curve estimates from prior days (or otherprior recurring time periods represented by the curve) in order to forman updated basal insulin sensitivity curve (ISF_(basal)). Referring toblock 426 of FIG. 4B, in some such embodiments, this basal insulinsensitivity factor curve is updated by computing:

${{ISF}_{basal} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{basal},n}}}},$

where, q is a predetermined number of historical updates to ISF_(basal),w is a linear or nonlinear vector of normalized weights, n is an integerindex into the historical updates to ISF_(basal) and vector w, andISF_(basal,n) is an n^(th) past ISF_(basal) calculation. For instance,in some embodiments, a basal insulin sensitivity curve estimateISF_(basal,n) representing an earlier period of time (e.g., an earlierday) is downweighted relative to a basal insulin sensitivity curveestimate ISF_(basal,n) representing a later day. This is done toemphasize the basal insulin sensitivity curve estimates from more recentdays, which are more likely to have significance determining the true abasal insulin sensitivity curve of the subject. This is accomplished inone embodiment, for example, by updating the basal insulin sensitivityfactor curve using the equation of block 426 by applying a first weightagainst the earlier basal insulin sensitivity curve estimateISF_(basal,n) and a second weight against the later basal insulinsensitivity curve estimate ISF_(basal,n) where the first weight is lessthan the second weight. In this way, the earlier basal insulinsensitivity curve estimate ISF_(basal,n) contributes less to the updatedinsulin sensitivity curve than the later basal insulin sensitivity curveestimate ISF_(basal,n). In some embodiments, the past seven insulinsensitivity curve estimate ISF_(basal,n) are combined in accordance withthe equation of block 426, where the oldest basal insulin sensitivitycurve estimate curve (ISF_(basal,n-7)) has the lowest weight, the mostrecent basal insulin sensitivity curve estimate ISF_(basal,n) has thehighest weight and the curve estimates between the oldest curve estimateand the most recent curve estimate are linearly scaled.

In another example of how the formula of block 426 is weighted in otherembodiments, each w_(n) is an independent weight for a correspondingISF_(basal,n), and each w_(n) is (i) equal to a first value when w_(n)weights a ISF_(basal,n) that is before a threshold date and (ii) equalto a second value when w_(n) weights a ISF_(basal,n) that is after athreshold date, and the first value is smaller than the second value. Insuch embodiments, each basal insulin sensitivity factor curve estimateISF_(basal,n) is multiplied by a corresponding weight to form the set{w₁a₁, w₂a₂, . . . , w_(R)a_(R)}, where each a_(n) in the set representsa ISF_(basal,n) and this set is summed to form the basal insulinsensitivity factor curve ISF_(basal). The weight w, of those basalinsulin sensitivity factor curve estimates ISF_(basal,n) that occurbefore the threshold date are each equal to a first value and thosebasal insulin sensitivity factor curve estimates ISF_(basal,n) thatoccur after the threshold date are each equal to a second value. In someembodiments, the threshold date is three days prior to the date of thepresent qualifying fasting event of the last instance of step 406-Yes,five days prior to the date of the present qualifying fasting event ofthe last instance of step 406-Yes, or seven days prior to the date ofthe present qualifying fasting event of the last instance of step406-Yes. In other words, in some embodiments, the updated basal insulinsensitivity factor curve ISF_(basal) formed using the basal insulinsensitivity factor curve estimates ISF_(basal,n) occurring more thanthree days ago, more than five days ago, or more than seven days ago areeach weighted against a first weight whereas basal insulin sensitivityfactor curve estimates ISF_(basal,n) formed more recently are eachweighted against a second weight. In some such embodiments, the firstvalue is zero and the second value is 1.

In some embodiments, the basal insulin sensitivity factor curveestimates ISF_(basal,n) are combined by taking a weighted average or ameasure of central tendency of the basal insulin sensitivity factorcurve estimate ISF_(basal,n) at each time t across the curves estimates.That is, for each time t in the curve, each of the past basal insulinsensitivity factor curve estimates ISF_(basal,n). are sampled at time tfor the basal insulin sensitivity factor at that time t and the weightedaverage or measure of central tendency of these values is used to form apoint for time t on the updated basal insulin sensitivity factor curve.In some embodiments, the measure of central tendency can be, forexample, an arithmetic mean, weighted mean, midrange, midhinge, trimean,Winsorized mean, median, or mode of such values. In some embodiments,the plurality of basal insulin sensitivity factor curve estimateISF_(basal,n) are combined into the basal insulin sensitivity factorcurve ISF_(basal) by taking a weighted average of the N most recentbasal insulin sensitivity factor curve estimates ISF_(basal,n) or ameasure of central tendency of the N most recent basal insulinsensitivity factor curve estimates ISF_(basal,n), where N is a positiveinteger (e.g., 1, 2, 3, 4, 5, 6, etc.). This measure of central tendencycan be, for example, an arithmetic mean, weighted mean, midrange,midhinge, trimean, Winsorized mean, median, or mode of such values.

Block 428. Turning to block 428 of FIG. 4C, in the case where acorrection bolus with a short acting insulin medicament occurs(408-Yes), a bolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232 ismade for the subject. This estimate makes use of (i) an expected bloodglucose level (BG_(expected)) based upon the correction bolus with theshort acting insulin medicament, (ii) the glucose level (

_(corr,i)) of the subject after occurrence of the correction bolus,where

_(corr,i) is obtained from the portion of the plurality of glucosemeasurements that is contemporaneous with a period of time after theoccurrence of the correction bolus, and (iii) an insulin sensitivityfactor (ISF_(bolus,i-p,t)) of the subject estimated based uponoccurrence of a prior correction bolus with the short acting insulinmedicament.

Referring to block 430 FIG. 4C, in some embodiments, this bolus insulinsensitivity estimate (ISF_(bolus,i,t)) 232 is computed as:

ISF bolus , i , t = ( BG expected - corr , i corr , i + 1 )  ISF bolus, i - p , t .

Computation of BG_(expected). In some embodiments BG_(expected) iscomputed as:

BG _(expected)=

_(hyp,i) −ISF _(bolus,i-p,t) U _(corr,i).

Here,

_(hyp,i) is a glucose measurement 222 of the subject contemporaneouswith a hyperglycaemic event after a meal event, or some other measure ofcentral tendency of a plurality of glucose measurements 222 of thesubject contemporaneous with the hyperglycaemic event.

_(hyp,i) is measured before the correction bolus is taken.ISF_(bolus,i-p,t) is the basal insulin sensitivity factor value taken attime t from the prior bolus insulin sensitivity estimate curveISF_(bolus,i-p), at time t (e.g., the time at which the subject actuallytook for the bolus that triggered the instant condition 408-Yes).U_(corr,i) is, in some embodiments, the dosage of the short actinginsulin medicament 214 for the current time period i (e.g., the currentdate/time 218) specified by the short acting insulin medicament regimen214, and, in other embodiments, is the amount of short acting insulinmedicament 214 the subject has actually taken for the bolus thattriggered the instant condition 408-Yes. U_(corr,i) is the correctionbolus necessary to bring the hyperglycaemic glucose level below an upperlimit and into a normal range of glucose levels.

_(hyp,i) can also be denoted

_(bolus,hyp,i) and

_(corr,i) can also be denoted

_(bolus,corr,i).

Calculation of

_(corr,i) In some embodiments

_(corr,i) is a blood glucose measurement during the time (t) of thebolus injection event 408-Yes, which is obtained from the portion of theplurality of glucose measurements in the first data set 220 that iscontemporaneous in time (t) with the bolus injection event thattriggered condition 408-Yes (the correction bolus). In some embodiments,the bolus event is measured over a period of three or more minutes, fiveor more minutes, between five minutes and thirty minutes or some otherperiod of time. As such, in some embodiments, there is more than oneglucose measurement 222 for the bolus event in the first data set 220.In some embodiments, when this is the case

_(corr,i) is an average value, or some other measure of centraltendency, of the plurality of glucose measurements 222 within the timeperiod. BG_(corr,i) is measured after the correction bolus is taken.

Calculation of ISF_(bolus,i-p,t). The value ISF_(bolus,i-p,t.) is theprior bolus insulin sensitivity factor of the subject during a qualifiedbolus event occurring before the present bolus event. The valueISF_(bolus,i-p,t.) can, for example, be obtained from sampling the valueat time (t) on the existing bolus insulin sensitivity curveISF_(bolus,i-p). With p set to 1, meaning take ISF_(bolus,i-p,t) fromthe prior recurring time period, such as the prior day, a value forISF_(bolus,i-1,t) is obtained for ISF_(bolus,i-p,t).

Block 432. Referring to block 432 of FIG. 4C, once the new bolus insulinsensitivity estimate (ISF_(bolus,i,t)) 232 is made, a bolus insulinsensitivity factor curve (ISF_(bolus,i)) 236 is estimated. Whereas thenew bolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232 representsbolus insulin sensitivity of the subject at the time of the correctionbolus, the bolus insulin sensitivity factor curve (ISF_(bolus,i)) 236represents the bolus insulin sensitivity factor of the subject over apredetermined recurring time period, such as the course of a day.However, the new bolus insulin sensitivity estimate (ISF_(bolus,i,t))232 is used to update the bolus insulin sensitivity factor curve(ISF_(bolus,i)) 236 in accordance with the teachings of the presentdisclosure. Referring to block 434 of FIG. 4C, in some embodiments, theestimating the bolus sensitivity factor curve 236 comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

where ISF_(bolus,i-p) represents a prior bolus sensitivity factor curveestimate.

Referring to block 436, in some embodiments, further estimates are made.For instance, in some embodiments the basal insulin sensitivity curve(ISF_(basal,i)) 234 is estimated as a function of the newly estimatedbolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232. That is, whenthe estimated bolus insulin sensitivity estimate (ISF_(bolus,i,t)) 232is made as described above, it is used to estimate the basal insulinsensitivity curve (ISF_(basal,i)) 234. Referring to block 438 of FIG.4C, in some embodiments, the estimating the basal insulin sensitivitycurve (ISF_(basal,i)) 234 as a function of the estimated bolus insulinsensitivity estimate (ISF_(bolus,i,t)) 232 comprises computing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

where ISF_(basal,i-p) represents a prior basal sensitivity factor curveestimate.

Referring to block 440 of FIG. 4C, the above embodiments describe thecomputation of a new estimated bolus insulin sensitivity curve(ISF_(bolus,i)) 236 for an i^(th) time period, such as an i^(th) day.Typically this i^(th) time period (e.g., this i^(th) day) is the presentday. In some embodiments, when a new estimated bolus insulin sensitivitycurve (ISF_(bolus,i)) 236 has been estimated, it is then combined withone or more bolus insulin sensitivity curve estimates from prior days(or prior recurring time periods) in order to form an updated bolusinsulin sensitivity curve (ISF_(bolus)). Referring to block 442 of FIG.4C, in some embodiments, this bolus insulin sensitivity factor curveupdated by computing:

${{ISF}_{bolus} = {\sum\limits_{n = {i - q}}^{i}{w_{n}{ISF}_{{bolus},n}}}},$

where q is a predetermined number of historical updates to the bolusinsulin sensitivity curve (ISF_(bolus)), w is a linear or nonlinearvector of normalised weights, n is an integer index into the historicalupdates to ISF_(bolus) and vector w, and ISF_(bolus,n) is an n^(th) pastbolus insulin sensitivity curve (ISF_(bolus)).

For instance, in some embodiments, a bolus insulin sensitivity curveestimate ISF_(bolus,n) representing an earlier period of time (e.g., anearlier day) is downweighted relative to a bolus insulin sensitivitycurve estimate ISF_(bolis,n) curve representing a later day. This isdone to emphasize the bolus insulin sensitivity curve estimates frommore recent days, which are more likely to have significance determiningthe true a bolus insulin sensitivity curve of the subject. This isaccomplished for example, in one embodiment, by updating the bolusinsulin sensitivity factor curve using the equation of block 442 byapplying a first weight against the earlier bolus insulin sensitivitycurve estimate ISF_(bolis,n) and a second weight against the later basalinsulin sensitivity curve estimate ISF_(bolis,n) where the first weightis less than the second weight. In this way, the earlier basal insulinsensitivity curve estimate ISF_(bolus,n) contributes less to the updatedinsulin sensitivity curve than the later basal insulin sensitivity curveestimate ISF_(bolis,n). In some embodiments, the past seven bolusinsulin sensitivity curve estimates ISF_(bolis,n) are combined inaccordance with the equation of block 442, where the oldest bolusinsulin sensitivity curve estimate (ISF_(bolus,n-7)) has the lowestweight, the most recent bolus insulin sensitivity curve estimateISF_(bolis,n) has the highest weight and the curve estimates between theoldest curve estimate and the most recent curve estimate are linearlyscaled.

In another example of how the formula of block 442 is weighted, eachw_(n) is an independent weight for a corresponding ISF_(bolus,n), andeach w_(n) is (i) equal to a first value when w_(n) weights aISF_(bolis,n) that is before a threshold date and (ii) equal to a secondvalue when w_(n) weights a ISF_(bolis,n) that is after a threshold date,and the first value is smaller than the second value. In suchembodiments, each bolus insulin sensitivity factor curve estimateISF_(bolus,n) is multiplied by a corresponding weight to form the set{w₁a₁, w₂a₂, . . . , w_(R)a_(R)}, where each a_(n) in the set representsa ISF_(bolis,n) and this set is summed to form the updated bolus insulinsensitivity factor curve ISF_(basal). The weight w_(i) of those bolusinsulin sensitivity factor curve estimates ISF_(bolis,n) that occurbefore the threshold date are each equal to a first value and thosebolus insulin sensitivity factor curve estimates ISF_(bolis,n) thatoccur after the threshold date are each equal to a second value. In someembodiments, the threshold date is three days prior to the date of thepresent qualifying bolus event of the last instance of step 408-Yes,five days prior to the date of the present qualifying bolus event of thelast instance of step 408-Yes, or seven days prior to the date of thepresent qualifying bolus event of the last instance of step 408-Yes. Inother words, in some embodiments, the updated bolus insulin sensitivityfactor curve ISF_(bolus) formed using the bolus insulin sensitivityfactor curve estimates ISF_(bolus) occurring more than three days ago,more than five days ago, or more seven days ago are each weightedagainst a first weight whereas bolus insulin sensitivity factor curveestimates ISF_(bolus,n) formed more recently are each weighted against asecond weight. In some such embodiments, the first value is zero and thesecond value is 1.

In some embodiments, the bolus insulin sensitivity factor curveestimates ISF_(bolus,n) are combined by taking a weighted average or ameasure of central tendency of the bolus insulin sensitivity factorcurve estimate ISF_(bolus,n) at each time t across the curve estimates.

That is, for each time t in the curve, each of the past bolus insulinsensitivity factor curve estimates ISF_(bolis,n) are sampled at time tfor the bolus insulin sensitivity factor at that time t and the weightedaverage or measure of central tendency of these values is used to form apoint for time t on the updated bolus insulin sensitivity factor curve.In some embodiments, the measure of central tendency can be, forexample, an arithmetic mean, weighted mean, midrange, midhinge, trimean,Winsorized mean, median, or mode of such values. In some embodiments,the plurality of bolus insulin sensitivity factor curve estimatesISF_(bolis,n) are combined into the bolus insulin sensitivity factorcurve ISF_(bolus) by taking a weighted average of the N most recentbolus insulin sensitivity factor curve estimates ISF_(bolis,n) or ameasure of central tendency of the N most recent basal insulinsensitivity factor curve estimates ISF_(bolis,n), where N is a positiveinteger (e.g., 1, 2, 3, 4, 5, 6, etc.). This measure of central tendencycan be, for example, an arithmetic mean, weighted mean, midrange,midhinge, trimean, Winsorized mean, median, or mode of such values.

Block 444. Advantageously, the disclosed techniques provide improvedbasal insulin sensitivity factor (ISF_(basal)) and bolus insulinsensitivity factor (ISF_(bolus)) curves. Referring to block 444 of FIG.4A, in some embodiments, a first recommended dose of the short actinginsulin medicament to achieve a target fasting glucose level in thesubject is provided by using glucose measurements from a portion of theplurality of glucose measurements and the updated bolus insulinsensitivity factor curve ISF_(bolus) or the updated basal insulinsensitivity factor curve ISF_(basal).

Referring to block 446, in some embodiments a third data set 238 isobtained that comprises a plurality of timestamped physiologicalmeasurements of the subject taken over the first period of time. In somesuch embodiments, a value of p for the quantity ISF_(basal,i-p,t) 412 orthe quantity ISF_(bolus,i-p,t) 430 is determined by the plurality ofphysiological measurements. For instance, referring to block 448 of FIG.4A, in some embodiments each physiological measurement 240 is ameasurement of body temperature of the subject and p is reduced duringperiods when the subject has an elevated temperature. As anotherexample, referring to block 450 of FIG. 4A, in some embodiments eachphysiological measurement is a measurement of activity of the subjectand p is reduced during periods when the subject is incurring elevatedactivity.

In some such embodiments, a value of q for the summation of block 426 orthe summation of block 442 is determined by the plurality ofphysiological measurements. For instance, in some embodiments eachphysiological measurement 240 is a measurement of body temperature ofthe subject and q is reduced during periods when the subject has anelevated temperature. As another example, in some embodiments eachphysiological measurement is a measurement of activity of the subjectand q is reduced during periods when the subject is incurring elevatedactivity.

Referring to FIG. 11, in some embodiments, the overall adherence of thesubject to an insulin medicament regimen is monitored using thetechniques disclosed in European patent application Nos. 16177082.1,16177083.9, and 16177090.4 each entitled “Systems and Methods forAnalysis of Insulin Regimen Adherence Data,” and filed Jun. 30, 2016,which are hereby incorporated by reference, and/or by the insulinregimen adherence techniques disclosed in Example 1. In some suchembodiments, when the insulin medicament regimen 206 adherence fallsbelow a threshold value (e.g., the insulin medicament regimen 206adherence drops below 90 percent of the injection events specified bythe insulin medicament regimen 206, the insulin medicament regimen 206adherence drops below 80 percent of the injection events specified bythe insulin medicament regimen 206, the insulin medicament regimenadherence drops below 70 percent of the injection events specified bythe insulin medicament regimen 206, or some other threshold value basedupon percentage of adherent injection events, fasting events and/or mealevents) the use of fasting events or bolus insulin injection events toupdate the insulin sensitivity factor curves ISF_(basal) andISF_(bolus), as set forth in FIGS. 4A through 4C, is suspended untilsuch time as the insulin medicament regimen 206 adherence rises abovethis a threshold value, as illustrated in FIG. 11.

Example 1: Use of glucose measurements to determine whether a bolusinjection event or a fasting event is insulin regimen adherent. In someembodiments, the first data set 220 comprising a plurality of glucosemeasurements is obtained. In some embodiments the glucose measurementsare obtain autonomously, for instance by a continuous glucose monitor.In this example, in addition to the autonomous glucose measurements,insulin administration events are obtained in the form of insulinmedicament records from one or more insulin pens 104 used by the subjectto apply the insulin medicament regimen 206. These insulin medicamentrecords may be in any format, and in fact may be spread across multiplefiles or data structures. As such, in some embodiments, the instantdisclosure leverages the recent advances of insulin administration pens,which have become “smart” in the sense that they can remember the timingand the amount of insulin medicament administered in the past. Oneexample of such an insulin pen 104 is the NovoPen 5. Such pens assistspatients in logging doses and prevent double dosing. It is contemplatedthat insulin pens will be able to send and receive insulin medicamentdose volume and timing, thus allowing the integration of continuousglucose monitors 102, insulin pens 104 and the algorithms of the presentdisclosure. As such, insulin medicament records from one or more insulinpens 104 is contemplated, including the wireless acquisition of suchdata from the one or more insulin pens 104.

In some embodiments, each insulin medicament record comprises: (i) arespective insulin medicament injection event including an amount ofinsulin medicament injected into the subject using a respective insulinpen in the one or more insulin pens and (ii) a corresponding electronictimestamp that is automatically generated by the respective insulin pen104 upon occurrence of the respective insulin medicament injectionevent.

In some embodiments, a fasting event is identified using the glucosemeasurements 222 of the subject and their associated glucose measurementtimestamps 224 in the first data set 220. Once a fasting event isidentified, by the method described for blocks 406 and 408 above, or anyother method, a classification is applied to the fasting event. Theclassification is one of “insulin regimen adherent” and “insulin regimennonadherent.”

A fasting event is deemed insulin regimen adherent when the acquired oneor more medicament records establish, on a temporal and quantitativebasis, adherence with the long acting insulin medicament regimen 208during the fasting event. A fasting event is deemed insulin regimennonadherent when the acquired one or more medicament records do notinclude one or more medicament records that establish, on a temporal andquantitative basis, adherence with the long acting insulin medicamentregimen during the fasting event. In some embodiments the long actinginsulin medicament regimen 208 specifies that a dose of long actinginsulin medicament 210 is to be taken during each respective epoch 212in a plurality of epochs and that a fasting event is deemed insulinregimen nonadherent when there are no medicament records for the epoch212 associated with the fasting event. In various embodiments, eachepoch in the plurality of epochs is two days or less, one day or less,or 12 hours or less. Thus, consider the case where the first data set220 is used to identify a fasting period and the long acting insulinmedicament regimen 208 specifies to take dosage A of a long actinginsulin medicament 210 every 24 hours. In this example, therefore, theepoch is one day (24 hours). The fasting event is inherently timestampedbecause it is derived from a period of minimum variance in timestampedglucose measurements, or by other forms of analysis of the timestampedglucose measurements 222. Thus the timestamp, or period of fasting,represented by a respective fasting event is used as a starting pointfor examining whether the fasting event is insulin regimen adherent. Forinstance, if the period of fasting associated with the respectivetimestamp is 6:00 AM on Tuesday, May 17, what is sought in themedicament injection records is evidence that the subject took dosage Aof the long acting insulin medicament in the 24 hour period (the epoch)leading up to 6:00 AM on Tuesday, May 17 (and not more or less of theprescribed dosage). If the subject took the prescribed dosage of thelong acting insulin medicament during this epoch, the fasting event(and/or the basal injection event and/or the glucose measurements duringthis time) is deemed insulin regimen adherent. If the subject did nottake the dose of the long acting insulin medicament 210 during thisepoch 212 (or took more than the dose of the long acting insulinmedicament during this period specified by the long acting insulinregimen 208), the fasting event (and/or the basal injection event and/orthe glucose measurements during this time) is deemed to be insulinregimen nonadherent.

In some embodiments, the epoch is defined by the long acting insulinmedicament regimen 208 and, so long as the subject took the amount ofbasal insulin required by the insulin medicament regimen 208 during theepoch (and not more), even if after the fasting event, the fasting eventwill be deemed insulin regimen adherent. For instance, if the epoch isone day beginning each day at just after midnight (in other words thelong acting insulin medicament regimen 208 specifies one or more longacting insulin medicament dosages to be taken each day, and furtherdefines a day as beginning and ending at midnight), and the fastingevent occurs at noon, the fasting event will be deemed insulin regimenadherent provided that the subject takes the long acting insulinmedicament injections prescribed for the day at some point during theday.

Continuing with this example, in some embodiments a meal event isidentified from the glucose measurements 222 and the correspondingtimestamps 224 in the first data 220 using a meal detection algorithm.Examples of such meal detection have been described above with referenceto blocks 406 and 408. In some embodiments, a bolus injection event isdeemed to be insulin regimen adherent when the injection event recordfor the bolus injection event indicates, on a temporal basis, aquantitative basis and a type of insulin medicament basis, adherencewith the short acting insulin medicament regimen 214 with respect to thedetected meal. In some embodiments, a bolus injection event is deemedinsulin regimen nonadherent when the medicament record for the bolusinjection event fails to indicate adherence, on a temporal basis, aquantitative basis, and a type of insulin medicament basis, with theshort acting insulin medicament regimen 214 for the detected meal. Forinstance, consider the case where the short acting insulin medicamentregimen 214 specifies that dosage A of insulin medicament B is to betaken up 30 minutes before a detected meal and that a meal that occurredat 7:00 AM on Tuesday, May 17. It will be appreciated that dosage A maybe a function of the anticipated size or type of meal. What is sought inthe medicament records is evidence that the subject took dosage A ofinsulin medicament B in the 30 minutes leading up to 7:00 AM on Tuesday,May 17 (and not more or less of the prescribed dosage). If the subjecttook the prescribed dosage A of the insulin medicament B during the 30minutes leading up to the respective meal as a bolus injection, thisbolus injection event will be deemed insulin regimen adherent. If thesubject took a dosage A of the insulin medicament B outside the 30minutes leading up to the respective meal (or it contained more than theprescribed dosage A of the insulin medicament B), the bolusadministration will be deemed insulin regimen nonadherent. The timeperiod of 30 minutes here is exemplary, in other embodiments the time isshorter or longer (e.g., between 15 minutes to 2 hours prior to the mealand/or is dependent upon the type of insulin medicament prescribed). Insome embodiments, the short acting insulin medicament regimen 214permits the bolus injection to be taken a short time after the meal.

In some embodiments, the short acting insulin medicament regimen 214specifies that the short acting insulin medicament is to be taken up toa predetermined amount of time prior to a meal. In some suchembodiments, a respective bolus injection event is deemed insulinregimen nonadherent when the respective bolus injection event occursthis permissible time. In some such embodiments, the predeterminedamount of time is thirty minutes or less, twenty minutes or less, orfifteen minutes or less.

Example 2. The following example is made with reference to FIGS. 10Athorough 10D which detail four simulations of blood glucose, insulininjections and insulin sensitivity factor estimation. In this example,35 days are simulated under the following set of conditions: three mealsare ingested each day (breakfast, lunch and dinner), (ii) a simple boluscalculator uses ISF and CIR to calculate bolus and correction dose,(iii) dinner bolus dose is forgotten 40% of the time (to allowcorrection doses more frequently), (iv) a correction bolus is given fourhours after a meal if glucose concentration is high, (v) insulinmedicament titration follows a simple 2-0-2 algorithm every second day,(vi) if a hypoglycaemic event occurs during the day, the basal istitrated down by 2U, (vii) during days 16-23 the insulin sensitivity isdecreased, to simulate flu or influenza, and (viii) during days 27-29insulin sensitivity increases, to simulate increased activity/exercise.

FIG. 10A illustrates the case of ISF updates based on correction bolusonly. Every time a correction bolus is given, the bolus insulinsensitivity factor (ISF_(bolus,i,t)) is updated in accordance with block428 and this updated value is used to make an insulin sensitivity factorcurve estimate (ISF_(bolus,i)) in accordance with block 432 and toupdate the bolus insulin sensitivity factor curve ISF_(bolus) inaccordance with block 442. The lower panel of FIG. 10A shows thenormalized true insulin sensitivity 1002 and a normalized estimate ofinsulin sensitivity factor ISF 1004, which is proportional to ISF_bolusand ISF_basal. The insulin sensitivity factor ISF is not updated afterday 22 since after that, no correction bolus is taken. Periods ofhypoglycaemia occur around day 28-31 where insulin sensitivity isincreasing, which is compensated for by reducing the basal, while theinsulin sensitivity factor estimate does not change and boluses arecalculated as before the increased sensitivity.

FIG. 10B illustrates the case where ISF updates are based on correctionbolus and fasting glucose in accordance with the methods set forth inFIG. 4. Every time a correction bolus is given, the bolus insulinsensitivity factor estimate (ISF_(bolus,i,t)) is made in accordance withblock 428 and this value is used to make an insulin sensitivity factorcurve estimate (ISF_(bolus,i)) in accordance with block 432 of and toupdate the bolus insulin sensitivity factor curve in accordance withblock 442 of FIG. 4C. Further, every time the patient is in bolusadherence and does not take a correction bolus at night, e.g. acorrection bolus that affects the fasting blood glucose, the basalinsulin sensitivity factor curve (ISF_(basal)) is updated based on thebasal injection and fasting glucose, according to blocks 410, 416 and426 of FIG. 4B. The lower panel of FIG. 10B shows the normalized trueinsulin sensitivity 1002, and a normalized estimate of the insulinsensitivity factor 1004, which is proportional to ISF_(bolus) andISF_(basal). From FIG. 10B it is observed that adding a basal ISFestimation improves the performance significantly. The algorithm adjuststo increased insulin sensitivity after the period of illness, as well asthe increased sensitivity following increased physical activity. Again,the basal is decreased but bolus calculations are also adjusted to thenew physiological state via the new ISF estimate.

FIG. 10C illustrates the case where the insulin sensitivity factorestimation is similar to that of FIG. 10B. However, here a temperaturemeasuring device indicates that the patient has a fever and that changesin insulin sensitivity factor are expected from day 16-23. An exercisedetector notices an increase in activity and announces that after day 27an increase in insulin sensitivity factor is expected. Information fromthe wearable devices are used to make new ISF estimates weigh higherthan past ISF estimates in accordance with the disclosure of blocks 426and 444, and the estimation horizon is made shorter. The lower panel ofFIG. 10C shows the normalized true insulin sensitivity 1002, and thenormalized estimate of the insulin sensitivity factor 1004, which isproportional to ISF_(bolus) and ISF_(basal). Thus, in FIG. 10C, thealgorithm had information about expected changes in ISF, and ISF andCIR, respectively. This allows the algorithm to more freely change theestimates based on new observations. It is observed that the estimatesfollow the curve of the true insulin sensitivity over time moreprecisely than in FIGS. 10A and 10B.

FIG. 10D illustrates the case where the insulin sensitivity factorestimation is similar to that of FIG. 10C. However, the carb-to-insulinratio CIR is changed proportionally to changes in ISF. When thealgorithm adjusted CIR and ISF, the hypoglycaemic events duringtransition in insulin sensitivity around day 30 are minimized comparedto the cases illustrated in FIGS. 10A, 10B, and 10C. The lower panel ofFIG. 10D shows the normalized true insulin sensitivity 1002, thenormalized estimate of the insulin sensitivity factor 1004, and theestimated carb-to-insulin ratio 1006. Here, the carb-to-insulin ratio isupdated proportionally to the ISF estimate as follows:

${CIR}_{i} = {\left( {\frac{{ISF}_{i,t} - {ISF}_{{i - p},t}}{{ISF}_{{i - p},t}} + 1} \right){{CIR}_{i - p}.}}$

LIST OF EMBODIMENTS

1. A device (250) for estimating parameters in an insulin medicamentregimen (206) for a subject that includes both a short acting insulinmedicament regimen (214) and a long acting insulin medicament regimen(208), and wherein the device comprises one or more processors and amemory, the memory storing instructions that, when executed by the oneor more processors, perform a method of:

-   -   A) obtaining:        -   A.1) a first data set (220), the first data set comprising a            plurality of glucose measurements of the subject taken over            a first period of time and, for each respective glucose            measurement (222) in the plurality of glucose measurements,            a timestamp (224) representing when the respective            measurement was made;        -   A.2) a second data set from one or more insulin pens used by            the subject to apply the insulin medicament dosage regimen,            the second data set comprises a plurality of insulin            medicament records, each insulin medicament record in the            plurality of medicament records comprises: (i) a respective            insulin medicament injection event including an amount of            insulin medicament injected into the subject using a            respective insulin pen in the one or more insulin pens (ii)            a corresponding electronic timestamp that is automatically            generated by the respective insulin pen upon occurrence of            the respective insulin medicament injection event, and (iii)            a type of insulin medicament, wherein the type of insulin            medicament is a short acting insulin medicament or a long            acting insulin medicament;    -   B) determining one or more insulin sensitivity change estimates        by:        -   B.1) making an estimate of a basal insulin sensitivity            change between a basal insulin sensitivity estimate            (ISF_(basal,i,t)) (230) for the subject related to the            occurrence of a first basal insulin related event undertaken            by the subject within the first period of time, when the            first basal insulin related event is deemed qualified, and a            basal insulin sensitivity estimate (ISF_(basal,i-p,t)) of            the subject related to the occurrence of a qualified basal            insulin related event occurring before the first basal            insulin related event, wherein the occurrence of the basal            insulin related events are identified in the first data set,            the estimating using:            -   (i) the basal insulin sensitivity estimate                (ISF_(basal,i-p,t)) of the subject related to the                occurrence of the qualified basal insulin related event                occurring before the first basal insulin related                event (ii) glucose measurements from the first data set                contemporaneous with the occurrence of the first                qualified basal insulin related event, (iii) glucose                measurements from the first data set contemporaneous                with the occurrence of the qualified basal insulin                related event occurring before the first qualified basal                insulin related event, (iv) an insulin medicament                injection event from the second data set corresponding                to the first qualified basal insulin related event,                wherein the injection has been applied according to the                long acting insulin medicament regimen (208), and (v) an                insulin medicament injection event from the second data                set corresponding to the qualified basal insulin related                event occurring before the first qualified basal insulin                related event, wherein the injection has been applied                according to the long acting insulin medicament regimen                (208); and/or        -   B.2) making an estimate of a bolus insulin sensitivity            change between a bolus insulin sensitivity estimate            (ISF_(bolus,i,t)) (232) for the subject relating to the            occurrence of a correction bolus with a short acting insulin            medicament within the first period of time and a bolus            insulin sensitivity estimate (ISF_(bolus,i-p,t)) of the            subject related to the occurrence of a prior correction            bolus with the short acting insulin medicament, wherein the            occurrence of the correction bolus and the occurrence of the            prior correction bolus are identified in the first data set,            the estimating using: (i) glucose measurements from the            first data set contemporaneous with the occurrence of the            correction bolus with a short acting medicament, (ii) the            bolus insulin sensitivity estimate (ISF_(bolus,i-p,t)) of            the subject related to the occurrence of a prior correction            bolus with the short acting insulin medicament and (iii) an            insulin medicament injection event from the second data set            corresponding to the occurrence of the correction bolus and            applied according to the short acting insulin medicament            regimen (214); and    -   C) estimating the bolus insulin sensitivity estimate        (ISF_(bolus,i,t)) as a function of the estimated basal insulin        sensitivity change, in response to making an estimate of the        basal insulin sensitivity change in B.1); and/or    -   D) estimating the basal insulin sensitivity estimate        (ISF_(basal,i,t)) as a function of the estimated bolus insulin        sensitivity change, in response to making an estimate of the        bolus insulin sensitivity change in B.2), and    -   wherein the insulin sensitivity estimates are parameters in the        insulin medicament regimen.

2. The device of embodiment 1, wherein the estimated basal insulinsensitivity change is a function of the estimated basal insulinsensitivity estimate (ISF_(basal,i,t)) for the subject upon occurrenceof the first basal insulin related event and the basal insulinsensitivity factor (ISF_(basal,i-p,t)) of the subject during thequalified basal insulin related event occurring before the first bolusinsulin relevant event.

3. The device of any of embodiments 1-2, wherein the estimated bolusinsulin sensitivity change is a function of the estimated bolus insulinsensitivity estimate (ISF_(bolus,i,t)) for the subject upon occurrenceof the correction bolus with a short acting insulin medicament and thebolus insulin sensitivity factor (ISF_(bolus,i-p,t)) of the subjectestimated based upon occurrence of a prior correction bolus with theshort acting insulin medicament.

4. The device of any of embodiments 1-3, the method further comprising:

-   -   E) estimating a basal insulin sensitivity factor curve        (ISF_(basal,i)) (234) as a function of the estimated basal        insulin change, in response to estimating the basal insulin        sensitivity change in B.1) and/or in response to estimating the        basal insulin sensitivity estimate in D).

5. The device of embodiment 4, wherein the estimating the basal insulinsensitivity factor curve (ISF_(basal,i)) in E) comprises computing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

wherein ISF_(basal,i-p) represents a prior basal sensitivity factorcurve estimate.

6. The device of any of embodiments 1-5, the method further comprising:

-   -   F) estimating a bolus insulin sensitivity factor curve        (ISF_(bolus,i)) (236) as a function of (i) the estimated bolus        insulin sensitivity change, in response to estimating the bolus        insulin sensitivity change in B2) and/or in response to        estimating the bolus insulin sensitivity in C).

7. The device of embodiments 6, wherein the estimating the bolussensitivity factor curve (ISF_(bolus,i)) in F) comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

wherein ISF_(bolus,i-p) represents a prior bolus sensitivity factorcurve estimate.

8. The device of any of embodiments 4-7, the method further comprising:

-   -   G) updating        -   (i) the bolus insulin sensitivity curve (ISF_(bolus)) as a            function of the estimated bolus insulin sensitivity factor            curve (ISF_(bolus,i)) of F) and prior estimated bolus            insulin sensitivity factor curves for the subject, and        -   (ii) updating the basal insulin sensitivity curve            (ISF_(basal)) as a function of the estimated basal insulin            sensitivity factor curve (ISF_(basal,i,t)) of E) and prior            estimated basal insulin sensitivity factor curves for the            subject; and    -   H) providing a recommended dose of the short acting insulin        medicament to achieve a target fasting glucose level in the        subject by using glucose measurements from a portion of the        plurality of glucose measurements and the updated bolus insulin        sensitivity curve (ISF_(bolus)) or the updated basal insulin        sensitivity curve (ISF_(basal)).

9. The device of any one of embodiments 1-8, wherein estimating thebasal insulin sensitivity change for the subject in B.1) is computed as:

( ISF basal , i ISF basal , i - p ) = ( FBG expected - i i + 1 ) ,

wherein FBG_(expected) is the expected blood glucose level(FBG_(expected)) in a period of time during the first qualified basalinsulin related event based on (i) the basal insulin sensitivityestimate (ISF_(basal,i-p,t)) of the subject related to the occurrence ofthe qualified basal insulin related event occurring before the firstbasal insulin related event (ii) the glucose measurements from the firstdata set contemporaneous with the occurrence of the qualified basalinsulin related event occurring before the first qualified basal insulinrelated event, (iii) the insulin medicament injection event from thesecond data set corresponding to the first qualified basal insulinrelated event, and (iv) the insulin medicament injection event from thesecond data set corresponding to the qualified basal insulin relatedevent occurring before the first qualified basal insulin related event,and wherein

_(i) is the glucose level (

_(i)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the first qualified basal insulinrelated event.

10. The device of any one of embodiments 1-8, wherein estimating thebasal insulin sensitivity change for the subject in B.1) is computed as:

( ISF basal , i , t ISF basal , i - p , t ) = ( i - i - p FBG expected -i - p ) ,

wherein

_(i) is the glucose level (

_(i)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the first qualified basal insulinrelated event, wherein FBG_(expected) is the expected blood glucoselevel (FBG_(expected)) during the first basal insulin related eventbased on (i) the basal insulin sensitivity estimate (ISF_(basal,i-p,t))of the subject related to the occurrence of the qualified basal insulinrelated event occurring before the first basal insulin related event(ii) the glucose measurements from the first data set contemporaneouswith the occurrence of the qualified basal insulin related eventoccurring before the first qualified basal insulin related event, (iii)the insulin medicament injection event from the second data setcorresponding to the first qualified basal insulin related event, and(iv) the insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, and wherein

_(i-p) is the glucose level (

_(i-p)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent, and wherein FBG_(expected) is different from

_(i-p).

11. The device of any of embodiments 9-10, wherein the expected bloodglucose level (FBG_(expected)) is computed as:

FBG _(expected)=

_(basal,i-p) −ISF _(basal,i-p,t)(U_(basal,i) −U _(basal,i-p)),

wherein U_(basal, i) is the amount of insulin medicament (U_(basal,i))corresponding to the insulin medicament injection event from the seconddata set corresponding to the first qualified basal insulin relatedevent, U_(basal, i-1) is the amount of insulin (U_(basal,i-p))corresponding to the insulin medicament injection event from the seconddata set corresponding to the qualified basal insulin related eventoccurring before the first qualified basal insulin related event.

12. The device of any one of embodiments 1-11, wherein the first basalinsulin related event is deemed qualified when (i) the subject has takenno correction bolus of the short acting insulin medicament in the twelvehours prior to the first basal insulin related event and (ii) thesubject has taken a meal bolus of the short acting insulin medicamentwith each hypoglycaemic event free meal in the fourteen hours prior tothe first fasting event, wherein the occurrence of a correction bolus, afirst basal insulin related event, a hypoglycaemic event free meal areidentified in the first data set

13. The device of embodiment 12, wherein the occurrence of a correctionbolus is further identified in the second data set.

14. The device of any one of embodiments 1-8, wherein the estimating thebolus insulin sensitivity change in B.2) is computed as:

( ISF bolus , i , t ISF bolus , i - p , t ) = ( BG expected - bolus ,corr , i bolus , corr , i + 1 ) ,

wherein BG_(expected) is the expected blood glucose level(BG_(expected)) based on (i) the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, (ii) the bolus insulin sensitivityestimate (ISF_(bolus,i-p,t)) of the subject related to the occurrence ofthe prior correction bolus with the short acting insulin medicament and(iii) an insulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus, and wherein

_(bolus,corr,i) is the glucose level (

_(bolus,corr,i)) of the subject after the occurrence of the correctionbolus, wherein

_(corr,i) is obtained from the portion of the glucose measurements ofthe first data set that are contemporaneous with a period of time afterthe occurrence of the correction bolus, and whereby the portion of theglucose measurements is a subset of the measurements that arecontemporaneous with the occurrence of the correction bolus with a shortacting medicament.

15. The device of any one of embodiments 1-8, wherein the estimating thebolus insulin sensitivity change in B.2) is computed as:

( ISF bolus , i , t ISF bolus , i - p , t ) = ( bolus , hyp , i - bolus, corr , i bolus , corr , i - BG expected ) ,

wherein

_(bolus,hyp,i) is the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, wherein

_(bolus,corr,i) is the glucose level (

_(corr,i)) of the subject after occurrence of the correction bolus,wherein

_(bolus,corr,i) is the glucose level (

_(bolus,corr,i)) of the subject after the occurrence of the correctionbolus, wherein

_(bolus,corr,i) is obtained from the portion of the glucose measurementsof the first data set that are contemporaneous with a period of timeafter the occurrence of the correction bolus, and whereby the portion ofthe glucose measurements is a subset of the measurements that arecontemporaneous with the occurrence of the correction bolus with a shortacting medicament, and wherein BG_(expected) is the expected bloodglucose level (BG_(expected)) based on (i) the glucose level

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, (ii) the bolus insulin sensitivityestimate (ISF_(bolus,i-p,t)) of the subject related to the occurrence ofthe prior correction bolus with the short acting insulin medicament and(iii) an insulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus.

15. The device of any of embodiments 14-15, wherein the expected bloodglucose level (BG_(expected)) is computed as:

BG _(expected)=

_(bolusl,hyp,i) −ISF _(bolus,i-p,t) U _(bolus,i).

16. The device of any of embodiments 1-15, wherein the estimating thebolus insulin sensitivity curve (ISF_(bolus,i)) as a function of theestimated basal insulin sensitivity change, in response to estimatingthe basal insulin sensitivity change in B.1) comprises computing:

${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$

wherein ISF_(bolus,i-p) represents a prior bolus sensitivity factorcurve estimate.

17. The device of any of embodiments 1-16, wherein the estimating thebasal insulin sensitivity curve (ISF_(basal,i)) as a function of theestimated bolus insulin sensitivity change, in response to estimatingthe bolus insulin sensitivity change in B2) comprises computing:

${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$

wherein ISF_(basal,i-p) represents a prior basal sensitivity factorcurve estimate.

18. The device of any of embodiments 6-7 and 16 wherein the updating thebolus insulin sensitivity factor curve comprises computing:

${{ISF}_{bolus} = {\sum\limits_{n = {i - q}}^{i}\; {w_{n}{ISF}_{{bolus},n}}}},$

wherein,

-   -   q is a predetermined number of historical updates to the bolus        insulin sensitivity curve (ISF_(bolus)),    -   w is a linear or nonlinear vector of normalised weights,    -   n is an integer index into the historical updates to the bolus        insulin sensitivity curve (ISF_(bolus)) and vector w, and    -   ISF_(bolus,n) is an n^(th) past bolus insulin sensitivity curve        (ISF_(bolus)) curve.

19. The device of any of embodiments 4-5 and 17 wherein the updating thebasal insulin sensitivity factor curve comprises computing:

${ISF}_{basal} = {\sum\limits_{n = {i - q}}^{i}\; {w_{n}{ISF}_{{basal},n}}}$

wherein,

-   -   q is a predetermined number of historical updates to the basal        insulin sensitivity curve (ISF_(basal)),    -   w is a linear or nonlinear vector of normalised weights,    -   n is an integer index into the historical updates to the basal        insulin sensitivity curve (ISF_(basal)) and vector w, and    -   ISF_(basal,n) is an n^(th) past basal insulin sensitivity curve        (ISF_(basal)).

20. The device of any one of embodiments 1-17, wherein the methodfurther comprises:

-   -   obtaining a third data set (238), the third data set comprising        a plurality of physiological measurements of the subject taken        over the first period of time and, for each respective        physiological measurement (240) in the plurality of        physiological measurements, a physiological measurement        timestamp (242) representing when the respective physiological        measurement was made; and wherein    -   a value of p is determined by the plurality of physiological        measurements.

21. The device of embodiment 20, wherein each physiological measurementis a measurement of body temperature of the subject and wherein p isreduced during periods when the subject has an elevated temperature.

22. The device of embodiment 20, wherein each physiological measurementis a measurement of activity of the subject and wherein p is reducedduring periods when the subject is incurring elevated activity.

23. A method for estimating parameters in an insulin medicament dosageregimen for a subject that includes both a short acting insulinmedicament regimen and a long acting insulin medicament regimen, themethod comprising:

-   -   A) obtaining:        -   A.1) a first data set (220), the first data set comprising a            plurality of glucose measurements of the subject taken over            a first period of time and, for each respective glucose            measurement (222) in the plurality of glucose measurements,            a timestamp (224) representing when the respective            measurement was made;        -   A.2) a second data set from one or more insulin pens used by            the subject to apply the insulin medicament dosage regimen,            the second data set comprises a plurality of insulin            medicament records, each insulin medicament record in the            plurality of medicament records comprises: (i) a respective            insulin medicament injection event including an amount of            insulin medicament injected into the subject using a            respective insulin pen in the one or more insulin pens (ii)            a corresponding electronic timestamp that is automatically            generated by the respective insulin pen upon occurrence of            the respective insulin medicament injection event, and (iii)            a type of insulin medicament, wherein the type of insulin            medicament is a short acting insulin medicament or a long            acting insulin medicament;    -   B) determining one or more insulin sensitivity change estimates        by:        -   B.1) making an estimate of a basal insulin sensitivity            change between a basal insulin sensitivity estimate            (ISF_(basal,i,t)) (230) for the subject related to the            occurrence of a first basal insulin related event undertaken            by the subject within the first period of time, when the            first basal insulin related event is deemed qualified, and a            basal insulin sensitivity estimate (ISF_(basal,i-p,t)) of            the subject related to the occurrence of a qualified basal            insulin related event occurring before the first basal            insulin related event, wherein the occurrence of the basal            insulin related events are identified in the first data set,            the estimating using:            -   (i) the basal insulin sensitivity estimate                (ISF_(basal,i-p,t)) of the subject related to the                occurrence of the qualified basal insulin related event                occurring before the first basal insulin related                event (ii) glucose measurements from the first data set                contemporaneous with the occurrence of the first                qualified basal insulin related event, (iii) glucose                measurements from the first data set contemporaneous                with the occurrence of the qualified basal insulin                related event occurring before the first qualified basal                insulin related event, (iv) an insulin medicament                injection event from the second data set corresponding                to the first qualified basal insulin related event,                wherein the injection has been applied according to the                long acting insulin medicament regimen (208), and (v) an                insulin medicament injection event from the second data                set corresponding to the qualified basal insulin related                event occurring before the first qualified basal insulin                related event, wherein the injection has been applied                according to the long acting insulin medicament regimen                (208); and/or        -   B.2) making an estimate of a bolus insulin sensitivity            change between a bolus insulin sensitivity estimate            (ISF_(bolus,i,t)) (232) for the subject relating to the            occurrence of a correction bolus with a short acting insulin            medicament within the first period of time and a bolus            insulin sensitivity estimate (ISF_(bolus,i-p,t)) of the            subject related to the occurrence of a prior correction            bolus with the short acting insulin medicament, wherein the            occurrence of the correction bolus and the occurrence of the            prior correction bolus are identified in the first data set,            the estimating using: (i) glucose measurements from the            first data set contemporaneous with the occurrence of the            correction bolus with a short acting medicament, (ii) the            bolus insulin sensitivity estimate (ISF_(bolus,i-p,t)) of            the subject related to the occurrence of a prior correction            bolus with the short acting insulin medicament and (iii) an            insulin medicament injection event from the second data set            corresponding to the occurrence of the correction bolus and            applied according to the short acting insulin medicament            regimen (214); and    -   C) estimating the bolus insulin sensitivity estimate        (ISF_(bolus,i,t)) as a function of the estimated basal insulin        sensitivity change, in response to making an estimate of the        basal insulin sensitivity change in B.1); and/or    -   D) estimating the basal insulin sensitivity estimate        (ISF_(basal,i,t)) as a function of the estimated bolus insulin        sensitivity change, in response to making an estimate of the        bolus insulin sensitivity change in B.2), and    -   wherein the insulin sensitivity estimates are parameters in the        insulin medicament regimen.

24. A computer program is provided comprising instructions that, whenexecuted by one or more processors, perform the method according toembodiment 23.

25. A computer-readable data carrier having stored thereon the computerprogram according to embodiment 24.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a nontransitorycomputer readable storage medium. For instance, the computer programproduct could contain the program modules shown in any combination ofFIG. 1, 2, or 3 and/or described in FIG. 4. These program modules can bestored on a CD-ROM, DVD, magnetic disk storage product, or any othernon-transitory computer readable data or program storage product.

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical applications, to thereby enable others skilled in the artto best utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. Theinvention is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled.

1. A device for estimating parameters in an insulin medicament regimenfor a subject that includes both a short acting insulin medicamentregimen and a long acting insulin medicament regimen, and wherein thedevice comprises one or more processors and a memory, the memory storinginstructions that, when executed by the one or more processors, performa method of: A) obtaining: A.1) a first data set, the first data setcomprising a plurality of glucose measurements of the subject taken overa first period of time and, for each respective glucose measurement inthe plurality of glucose measurements, a timestamp representing when therespective measurement was made; A.2) a second data set from one or moreinsulin pens used by the subject to apply the insulin medicament dosageregimen, the second data set comprises a plurality of insulin medicamentrecords, each insulin medicament record in the plurality of medicamentrecords comprises: (i) a respective insulin medicament injection eventincluding an amount of insulin medicament injected into the subjectusing a respective insulin pen in the one or more insulin pens (ii) acorresponding electronic timestamp that is automatically generated bythe respective insulin pen upon occurrence of the respective insulinmedicament injection event, and (iii) a type of insulin medicament,wherein the type of insulin medicament is a short acting insulinmedicament or a long acting insulin medicament; B) determining one ormore insulin sensitivity change estimates by: B.1) making an estimate ofa basal insulin sensitivity change between a basal insulin sensitivityestimate (ISF_(basal,i,t)) for the subject related to the occurrence ofa first basal insulin related event undertaken by the subject within thefirst period of time, when the first basal insulin related event isdeemed qualified, and a basal insulin sensitivity estimate(ISF_(basal,i-p,t)) of the subject related to the occurrence of aqualified basal insulin related event occurring before the first basalinsulin related event, wherein the occurrence of the basal insulinrelated events are identified in the first data set, the estimatingusing: (i) the basal insulin sensitivity estimate (ISF_(basal,i-p,t)) ofthe subject related to the occurrence of the qualified basal insulinrelated event occurring before the first basal insulin related event(ii) glucose measurements from the first data set contemporaneous withthe occurrence of the first qualified basal insulin related event, (iii)glucose measurements from the first data set contemporaneous with theoccurrence of the qualified basal insulin related event occurring beforethe first qualified basal insulin related event, (iv) an insulinmedicament injection event from the second data set corresponding to thefirst qualified basal insulin related event, wherein the injection hasbeen applied according to the long acting insulin medicament regimen,and (v) an insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, wherein theinjection has been applied according to the long acting insulinmedicament regimen; and/or B.2) making an estimate of a bolus insulinsensitivity change between a bolus insulin sensitivity estimate(ISF_(bolus,i,t)) for the subject relating to the occurrence of acorrection bolus with a short acting insulin medicament within the firstperiod of time and a bolus insulin sensitivity estimate(ISF_(bolus,i-p,t)) of the subject related to the occurrence of a priorcorrection bolus with the short acting insulin medicament, wherein theoccurrence of the correction bolus and the occurrence of the priorcorrection bolus are identified in the first data set, the estimatingusing: (i) glucose measurements from the first data set contemporaneouswith the occurrence of the correction bolus with a short actingmedicament, (ii) the bolus insulin sensitivity estimate(ISF_(bolus,i-p,t)) of the subject related to the occurrence of a priorcorrection bolus with the short acting insulin medicament, and (iii) aninsulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus and appliedaccording to the short acting insulin medicament regimen; and C)estimating the bolus insulin sensitivity estimate (ISF_(bolus,i,t)) as afunction of the estimated basal insulin sensitivity change, in responseto making an estimate of the basal insulin sensitivity change in B.1);and/or D) estimating the basal insulin sensitivity estimate(ISF_(basal,i,t)) as a function of the estimated bolus insulinsensitivity change, in response to making an estimate of the bolusinsulin sensitivity change in B.2), and wherein the insulin sensitivityestimates are parameters in the insulin medicament regimen.
 2. Thedevice of claim 1, wherein the estimated basal insulin sensitivitychange is a function of the estimated basal insulin sensitivity estimate(ISF_(basal,i,t)) for the subject upon occurrence of the first basalinsulin related event and the basal insulin sensitivity factor(ISF_(basal,i-p,t)) of the subject during the qualified basal insulinrelated event occurring before the first bolus insulin relevant event.3. The device of claim 1, wherein the estimated bolus insulinsensitivity change is a function of the estimated bolus insulinsensitivity estimate (ISF_(bolus,i,t)) for the subject upon occurrenceof the correction bolus with a short acting insulin medicament and thebolus insulin sensitivity factor (ISF_(bolus,i-p,t)) of the subjectestimated based upon occurrence of a prior correction bolus with theshort acting insulin medicament.
 4. The device of claim 1, the methodfurther comprising: E) estimating a basal insulin sensitivity factorcurve (ISF_(basal,i)) as a function of the estimated basal insulinchange, in response to estimating the basal insulin sensitivity changein B.1) and/or in response to estimating the basal insulin sensitivityestimate in D).
 5. The device of claim 4, wherein the estimating thebasal insulin sensitivity factor curve (ISF_(basal,i)) in E) comprisescomputing:${{ISF}_{{basal},i} = {\left( {\frac{{ISF}_{{basal},i,t} - {ISF}_{{basal},{i - p},t}}{{ISF}_{{basal},{i - p},t}} + 1} \right){ISF}_{{basal},{i - p}}}},$wherein ISF_(basal,i-p) represents a prior basal sensitivity factorcurve estimate.
 6. The device of claim 1, the method further comprising:F) estimating a bolus insulin sensitivity factor curve (ISF_(bolus,i))as a function of (i) the estimated bolus insulin sensitivity change, inresponse to estimating the bolus insulin sensitivity change in B2)and/or in response to estimating the bolus insulin sensitivity in C). 7.The device of claim 6, wherein the estimating the bolus sensitivityfactor curve (ISF_(bolus,i)) in F) comprises computing:${{ISF}_{{bolus},i} = {\left( {\frac{{ISF}_{{bolus},i,t} - {ISF}_{{bolus},{i - p},t}}{{ISF}_{{bolus},{i - p},t}} + 1} \right){ISF}_{{bolus},{i - p}}}},$wherein ISF_(bolus,i-p) represents a prior bolus sensitivity factorcurve estimate.
 8. The device of claim 4, the method further comprising:G) updating: (i) the bolus insulin sensitivity curve (ISF_(bolus)) as afunction of the estimated bolus insulin sensitivity factor curve(ISF_(bolus,i)) of F) and prior estimated bolus insulin sensitivityfactor curves for the subject, and (ii) updating the basal insulinsensitivity curve (ISF_(basal)) as a function of the estimated basalinsulin sensitivity factor curve (ISF_(basal,i)) of E) and priorestimated basal insulin sensitivity factor curves for the subject; andH) providing a recommended dose of the short acting insulin medicamentto achieve a target fasting glucose level in the subject by usingglucose measurements from a portion of the plurality of glucosemeasurements and the updated bolus insulin sensitivity curve(ISF_(bolus)) or the updated basal insulin sensitivity curve(ISF_(basal)).
 9. The device of claim 1, wherein estimating the basalinsulin sensitivity change for the subject in B.1) is computed as: ( ISFbasal , i ISF basal , i - p ) = ( FBG expected - i i + 1 ) , whereinFBG_(expected) is the expected blood glucose level (FBG_(expected)) in aperiod of time during the first qualified basal insulin related eventbased on: (i) the basal insulin sensitivity estimate (ISF_(basal,i-p,t))of the subject related to the occurrence of the qualified basal insulinrelated event occurring before the first basal insulin related event,(ii) the glucose measurements from the first data set contemporaneouswith the occurrence of the qualified basal insulin related eventoccurring before the first qualified basal insulin related event, (iii)the insulin medicament injection event from the second data setcorresponding to the first qualified basal insulin related event, and(iv) the insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, and wherein

_(i) is the glucose level (

_(i)) obtained from the glucose measurements from the first data setcontemporaneous with the occurrence of the first qualified basal insulinrelated event.
 10. The device of claim 9, wherein the expected bloodglucose level (FBG_(expected)) is computed as:FBG _(expected)=

_(basal,i-p) −ISF _(basal,i-p,t)(U _(basal,i) −U _(basal,i-p)), wherein

_(basal,i-p) is the glucose level (

_(basal,i-p)) based on the glucose measurements from the first data setcontemporaneous with the occurrence of the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent, wherein U_(basal, i) is the amount of insulin medicament(U_(basal,i)) corresponding to the insulin medicament injection eventfrom the second data set corresponding to the first qualified basalinsulin related event, U_(basal, i-1) is the amount of insulin(U_(basal,i-p)) corresponding to the insulin medicament injection eventfrom the second data set corresponding to the qualified basal insulinrelated event occurring before the first qualified basal insulin relatedevent.
 11. The device of claim 1, wherein the first basal insulinrelated event is deemed qualified when: (i) the subject has taken nocorrection bolus of the short acting insulin medicament in the twelvehours prior to the first basal insulin related event, and (ii) thesubject has taken a meal bolus of the short acting insulin medicamentwith each hypoglycaemic event free meal in the fourteen hours prior tothe first fasting event, wherein the occurrence of a correction bolus, afirst basal insulin related event, a hypoglycaemic event free meal areidentified in the first data set.
 12. The device of claim 1, wherein theestimating the bolus insulin sensitivity change in B.2) is computed as:( ISF bolus , i , t ISF bolus , i - p , t ) = ( BG expected - bolus ,corr , i bolus , corr , i + 1 ) , wherein BG_(expected) is the expectedblood glucose level (BG_(expected)) based on: (i) the glucose level (

_(bolus,hyp,i)) of the subject after a meal event, wherein

_(bolus,hyp,i) is obtained from the portion of the glucose measurementsobtained from the first data set that are contemporaneous with a periodof time during a hyperglycaemic event after a meal event, and wherebythe portion of the glucose measurements is a subset of the measurementsthat are contemporaneous with the occurrence of the correction boluswith a short acting medicament, (ii) the bolus insulin sensitivityestimate (ISF_(bolus,i-p,t)) of the subject related to the occurrence ofthe prior correction bolus with the short acting insulin medicament, and(iii) an insulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus, and wherein

_(bolus,corr,i) is the glucose level (

_(bolus,corr,i)) of the subject after the occurrence of the correctionbolus, wherein

_(bolus,corr,i) is obtained from the portion of the glucose measurementsof the first data set that are contemporaneous with a period of timeafter the occurrence of the correction bolus, and whereby the portion ofthe glucose measurements is a subset of the measurements that arecontemporaneous with the occurrence of the correction bolus with a shortacting medicament.
 13. The device of claim 12, wherein the expectedblood glucose level (BG_(expected)) is computed as:BG _(expected)=

_(bolusl,hyp,i) −ISF _(bolus,i-p,t) U _(bolus,i).
 14. A method forestimating parameters in an insulin medicament dosage regimen for asubject that includes both a short acting insulin medicament regimen anda long acting insulin medicament regimen, the method comprising: A)obtaining: A.1) a first data set, the first data set comprising aplurality of glucose measurements of the subject taken over a firstperiod of time and, for each respective glucose measurement in theplurality of glucose measurements, a timestamp representing when therespective measurement was made; A.2) a second data set from one or moreinsulin pens used by the subject to apply the insulin medicament dosageregimen, the second data set comprises a plurality of insulin medicamentrecords, each insulin medicament record in the plurality of medicamentrecords comprises: (i) a respective insulin medicament injection eventincluding an amount of insulin medicament injected into the subjectusing a respective insulin pen in the one or more insulin pens, (ii) acorresponding electronic timestamp that is automatically generated bythe respective insulin pen upon occurrence of the respective insulinmedicament injection event, and (iii) a type of insulin medicament,wherein the type of insulin medicament is a short acting insulinmedicament or a long acting insulin medicament; B) determining one ormore insulin sensitivity change estimates by: B.1) making an estimate ofa basal insulin sensitivity change between a basal insulin sensitivityestimate (ISF_(basal,i,t)) for the subject related to the occurrence ofa first basal insulin related event undertaken by the subject within thefirst period of time, when the first basal insulin related event isdeemed qualified, and a basal insulin sensitivity estimate(ISF_(basal,i-p,t)) of the subject related to the occurrence of aqualified basal insulin related event occurring before the first basalinsulin related event, wherein the occurrence of the basal insulinrelated events are identified in the first data set, the estimatingusing: (i) the basal insulin sensitivity estimate (ISF_(basal,i-p,t)) ofthe subject related to the occurrence of the qualified basal insulinrelated event occurring before the first basal insulin related event,(ii) glucose measurements from the first data set contemporaneous withthe occurrence of the first qualified basal insulin related event, (iii)glucose measurements from the first data set contemporaneous with theoccurrence of the qualified basal insulin related event occurring beforethe first qualified basal insulin related event, (iv) an insulinmedicament injection event from the second data set corresponding to thefirst qualified basal insulin related event, wherein the injection hasbeen applied according to the long acting insulin medicament regimen,and (v) an insulin medicament injection event from the second data setcorresponding to the qualified basal insulin related event occurringbefore the first qualified basal insulin related event, wherein theinjection has been applied according to the long acting insulinmedicament regimen; and/or B.2) making an estimate of a bolus insulinsensitivity change between a bolus insulin sensitivity estimate(ISF_(bolus,i,t)) for the subject relating to the occurrence of acorrection bolus with a short acting insulin medicament within the firstperiod of time and a bolus insulin sensitivity estimate(ISF_(bolus,i-p,t)) of the subject related to the occurrence of a priorcorrection bolus with the short acting insulin medicament, wherein theoccurrence of the correction bolus and the occurrence of the priorcorrection bolus are identified in the first data set, the estimatingusing: (i) glucose measurements from the first data set contemporaneouswith the occurrence of the correction bolus with a short actingmedicament, (ii) the bolus insulin sensitivity estimate(ISF_(bolus,i-p,t)) of the subject related to the occurrence of a priorcorrection bolus with the short acting insulin medicament, and (iii) aninsulin medicament injection event from the second data setcorresponding to the occurrence of the correction bolus and appliedaccording to the short acting insulin medicament regimen; and C)estimating the bolus insulin sensitivity estimate (ISF_(bolus,i,t)) as afunction of the estimated basal insulin sensitivity change, in responseto making an estimate of the basal insulin sensitivity change in B.1);and/or D) estimating the basal insulin sensitivity estimate(ISF_(basal,i,t)) as a function of the estimated bolus insulinsensitivity change, in response to making an estimate of the bolusinsulin sensitivity change in B.2), and wherein the insulin sensitivityestimates are parameters in the insulin medicament regimen.
 15. Acomputer program is provided comprising instructions that, when executedby one or more processors, perform the method according to claim 14.